Compressive sensing (CS) technology has proven highly effective in rapid data acquisition and super-resolution target imaging for through-the-wall radar imaging (TWRI) applications. However, most existing CS-based TWRI techniques focus primarily on high-quality imaging of behind-wall targets, often neglecting the reconstruction of building layouts, which is essential for determining the relative positions of targets in unknown environments. To address this limitation, a structural-prior-guided CS framework is proposed for the joint reconstruction of building layouts and behind-wall targets. Specifically, first, distinct imaging models are developed for layouts and targets, accounting for their unique structural properties: layouts, referring to wall structures, typically manifest as extended, piecewise-continuous line-like structures, whereas targets manifest as compact, point-like structures. Building on these models, a unified constrained optimization problem is formulated by integrating (i) the strong inter-channel correlation of layout echoes, enforced via a low-rank regularization on the layout component, and (ii) structured sparsity priors tailored to both the layout and target images. Then, the resulting composite problem is efficiently solved using proximal gradient algorithm, yielding simultaneous reconstruction of the unknown building layouts and behind-wall targets. Finally, simulations and experimental results demonstrate the effectiveness of the proposed algorithm.

In synthetic aperture radar (SAR) target recognition, the interpretation of SAR images and the accurate recognition of targets are significantly affected by speckle noise. Traditional recognition methods often fail to meet the high accuracy requirements. Therefore, this letter proposes a dual-ended fusion network (DEFNet) for SAR target recognition. The network consists of three main components: the local refinement feature extraction (LRFE) network, the large-scale feature extraction (LSFE) network, and the adaptive feature interaction fusion (AFIF) module. It employs a parallel structure, utilizing the LRFE and LSFE branch modules to extract local and large-scale features, respectively. These two features are then adaptively fused through the AFIF module to further enhance feature representation. Experimental results on the moving and stationary target acquisition and recognition dataset indicate that DEFNet demonstrates significant performance improvement compared to traditional methods, showcasing its effectiveness and adaptability in SAR target recognition tasks.

This article considers the dynamic time resource scheduling problem for a phased array radar performing joint search and tracking tasks in an adversarial jamming environment. Uniform resource scheduling in such conditions leads to security-critical failures, including the missed detection of weak threats and loss of tracking. To address this challenge, a multi-step dynamic optimization model with a receding-horizon strategy is formulated for joint search-and-tracking time resource scheduling under adversarial jamming. Then, a framework integrating Pontryagin’s maximum principle and the alternating direction method of multipliers (ADMM) is developed to solve the resulting problem. The maximum principle produces future-aware adjoint signals that quantify the future risk of jamming-induced sensing loss, while ADMM enforces per-stage feasibility and handles the non-convex optimization problem. Numerical simulations are conducted to verify that the proposed algorithm maintains higher tracking accuracy, stronger jamming resilience, and higher total utility than several comparison methods, thereby improving sensing robustness in adversarial conditions.

Millimeter-wave (mmWave) radar and camera are two of the most critical sensors in modern intelligent transportation systems (ITS), enabling complex fusion-based perception tasks through collaborative working. Achieving high-quality data fusion in ITS requires precise extrinsic parameters (EPs), which define the relative spatial relationship between sensors. However, in real traffic environments, manual measurement of EPs between sensors is labor-intensive and limited in accuracy. To overcome these challenges, this paper proposes an automatic extrinsic calibration method for mmWave radar and camera in traffic environments, requiring only time-synchronized sensor data. First, we develop a novel calibration model with eight parameters, the radar-camera-ground (RCG) model, which describes the spatial relationships between the sensors and the ground. Then, a calibration method named Gaussian Modeling Linear Optical Projection (GLP) is proposed. Specifically, an image instance segmentation model is applied to detect targets from images. Simultaneously, 2D information of targets detected by the radar is extended into 3D space according to the RCG Model. Next, vehicle targets detected by radar and camera are transformed into corresponding 3D and 2D Gaussian models, respectively, leveraging their positional, velocity, and shape features. Afterward, a mapping relationship between the 3D and 2D Gaussian models is established through a linearized optical projection function. Finally, the optimal EPs are estimated via the global optimization algorithm, minimizing the designed calibration loss function based on Bhattacharyya distance. Experimental results on practical traffic scenario data demonstrate that the proposed method outperforms existing approaches in average calibration accuracy and robustness, validating its reliability and superiority.

In this paper, a vision-assisted multipath recognition and suppression method is proposed for the problem of millimeter wave (mmWave) radar producing false targets under the influence of multipath interference. First, object detection is performed on the image and rectangular clustering is performed on the mmWave radar point cloud to complete the data preprocessing. Subsequently, nearest-neighbor frame matching and direct linear transform (DLT) algorithms are used to achieve spatio-temporal calibration of the two sensors. An axial adaptive cost-normalized matching algorithm is then proposed to associate targets from the two sensors, thereby establishing target association pairs. Finally, multipath ghosts in mmWave radar are recognized and suppressed based on the target association results. Experimental results show that the proposed method efficiently recognizes and suppresses multipath ghosts in traffic scenarios.

Non-line-of-sight (NLOS) detection is crucial in applications such as autonomous driving and surveillance. This paper proposes a bistatic multiple-input multiple-output (MIMO) radar-based joint estimation algorithm to localize diffraction corners and estimate NLOS targets. By leveraging the direction of departure (DoD) and direction of arrival (DoA) of diffraction signal, the algorithm first estimates corner position. Furthermore, target motion state is estimated based on the estimated corner and Doppler information. Electromagnetic simulations confirm the accuracy and robustness of the proposed method under various noise conditions.

Low probability of intercept (LPI) radar signals are widely used in modern electromagnetic warfare due to their exceptional anti-interception capabilities. A defining characteristic of LPI radar signals is their low peak power, which makes them highly susceptible to being masked by additive white Gaussian noise (AWGN), causing low signal-to-noise ratio (SNR) levels challenging their detection, recognition and parameter estimation. To recover the original LPI signals from the AWGN background, this paper proposes a novel deep neural network (DNN) for end-to-end time-domain enhancement of LPI radar signals, named ETDE-Net, which consists of a feature extraction module (FEM) and a signal restoration module (SRM). The FEM acquires the representative signal features with reshape operation, channel attention and linear layers, while the SRM can recover the waveform of LPI signals by capturing the pulse information using convolutional neural networks (CNNs) and state space models (SSMs). ETDE-Net is the first DNN to achieve end-to-end time-domain LPI radar signals enhancement with superior performance compared to typical filter-based and DNN-based methods. Simulation results show that ETDE-Net has excellent signal enhancement performance at low SNRs, validating its feasibility and effectiveness.

In the realm of human activity recognition (HAR) based on radar, the prevailing methods have been characterized by excessive complexity and a singular focus on a specific motion direction, posing challenges for practical deployment. This article introduces a lightweight parameter estimation and deep learning hybrid extraction network (PDHE-Net) for achieving multidirectional HAR based on mmWave radar. The network consists of a lightweight deep learning feature extraction (LDE) module, a parameter estimation module, and a classification module. Specifically, the LDE module consists of multiple layers of group convolution and Ghost module, aimed at extracting deep features from the time-Doppler (TD) maps. Parameter estimation module is employed to capture direction-independent features from the TD map. Ultimately, multidimensional features extracted by the LDE module and parameter estimation module are classified to realize multidirectional HAR. To verify the performance of the proposed method, experimental data was collected, comprising six categories of activities wherein targets moved in multidirections. The experimental results demonstrate that the proposed PDHE-Net achieved a recognition accuracy of 96.67% on the dataset, outperforming the state-of-the-art methods by 2.92%, while significantly reducing complexity.

Radar-based human activity recognition (HAR) holds significant application value in fields such as medical rehabilitation and security monitoring. However, existing HAR methods primarily address line-of-sight (LOS) and non-line-of-sight through-wall (NLOS-TW) scenarios, neglecting consideration for non-line-of-sight corner (NLOS-C) scenario within urban architecture. In NLOS-C scenario, electromagnetic waves illuminate the target through multiple paths, resulting in considerable variations in range-time map, causing performance degradation or even failure of existing methods. Moreover, multipath propagation enables a single-node radar to function as a multi-perspective multi-node radar system, providing more comprehensive information for human activity. Therefore, considering the complementary interpretations of multipath and the distinctive features observed in NLOS-C range-time map, this paper proposes a HAR method for NLOS-C scenario based on multipath fusion. Firstly, considering the broad distribution and large span characteristics of behavior features in the range dimension caused by multipath effect, we design a multipath information fusion module based on dilated convolution to effectively integrate and interact the multipath information. Additionally, to address the diverse feature scales caused by variable widths and blurred boundaries of each path, we incorporate multi-scale unit into the deep feature extraction module to enhance the capability of autonomously adjusting receptive field. Finally, multipath interaction information is fused with depth features for recognition. Experimental results validate the effectiveness of the proposed method in NLOS-C scenario. The codes are available at https://github.com/tlz1111/Multipath-Fusion-Network.

Traditional radar signal modulation recognition (RSMR) methods struggle to achieve the required accuracy under low signal-to-noise ratio (SNR) conditions. To address this issue, a hybrid network architecture integrating deformable convolution and mamba (DCMNet) is proposed. Specifically, DCMNet employs a multi-view feature extraction structure that combines inverted deformable convolution (IDC) with a state space model (SSM), enabling dynamic adjustment of convolution kernel positions and capturing global information and dependencies in long sequence data. The cross-gated feature fusion (CGFF) mechanism effectively modulates and dynamically aggregates features from different perspectives. The lightweight design provides significant advantages in terms of network scale and deployment. Experimental results demonstrate that the proposed method achieves excellent performance on a dataset with ten different waveforms. Notably, at an SNR of -8 dB, the recognition accuracy exceeds 90%, significantly outperforming existing methods.

In this paper, aimed to protect the transmitted waveform from being intercepted by advanced electronic support measure systems, a low probability of intercept (LPI) signal design, taking into account the transmitted waveform and precoder of communication users, is developed for multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems. The considered system simultaneously senses multiple targets and communicates with multiple users in the presence of various interferences. Specifically, based on the analysis of cyclic spectrum, the desired LPI performance is achieved by minimizing the cyclic frequency sidelobe level. Moreover, sensing and communication requirements are satisfied by respectively maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of targets and constraining the received SINR lower bound of each user. Energy budget and peak-to-average power ratio constraints are introduced to meet the practical resources limits, while similarity constraint is constructed to further ensure the target sensing. Next, in order to solve the formulated nonconvex problem, an efficient iterative algorithm is proposed based on the idea of successive convex approximation (SCA). Finally, simulation results demonstrate the effectiveness of our proposed scheme and display the trade-off among LPI, sensing and communication performance.

Polarization adds an additional dimension to the radar signals, contributing to waveform diversity. Codesign of unimodular waveforms and filters with polarimetric power allocation for maximizing the signal-to-interference-plus-noise ratio (SINR) plays a key role in the polarimetric radar system. The problem is challenging to solve due to the nonconvex nature of the objective function and constraints, coupled with the interdependence of multiple variables. Existing methods mainly solve this problem by fixing the power allocation or relaxing the objective function and obtaining the receive filters with matrix inversion. We directly address this problem without matrix inversion by using the proposed adaptive unified manifold optimization (AUMO) framework. Specifically, a unified manifold space (UMS) is constructed to satisfy the constraints of unimodular waveform, filters, and power, transforming the problem to an unconstrained optimization problem over the manifold. To solve this problem, a parallel conjugate gradient (PCG) algorithm is derived. This algorithm can adaptively change the step size by exploring the local features of the manifold space. The experimental results based on the measured data show that the proposed method outperforms existing methods in terms of SINR gain and execution time.

Compared with the monostatic radar which only performs signal fusion in the multi-pulse dimension, the bistatic multiple-input multiple-output (MIMO) radar can also fuse multi-channel signals to improve the detection ability of high-speed moving targets. However, how to tackle the range migration (RM) caused by high-speed motion and compensate the signal difference between channels are the key problems in bistatic MIMO radar. To solve these problems, this paper proposes a computationally efficient multi-channel multi-pulse fusion algorithm. Firstly, we establish the echo model of bistatic MIMO radar with high-speed moving targets and utilize the Radon Fourier transform (RFT) algorithm to overcome the RM and complete the multi-pulse fusion. Then, the output characteristics of RFT, including peak and phase differences, are analyzed in detail. On this basis, a multi-channel coherent fusion method based on geometric information is proposed, which can eliminate the phase differences among channels and complete coherent fusion. Finally, numerical experiments show that the proposed algorithm can effectively detect the target under a low signal-to-noise ratio (SNR). Compared with the existing methods, the proposed algorithm can achieve a balance between computational complexity and detection performance.

The traditional two-step localization method for transmitter requires to estimate the signal parameters such as angle of arrival (AOA) and time of arrival (TOA), wich confronts the problem of localization error cumulation. While the direct position determination (DPD) can effectively reduce the estimation error and achieve superior localization performance than the two-step localization, which is widely applied in passive radar. Unfortunately, the uncertainty of the transmission signal parameters will causes the localization performance degradation for DPD method in passive localization. To address these issues, this paper considers the LFM signal transmitter localization with passive radar. Firstly, the signal within each receiving channel is accumulated by fractional Fourier transform (FrFT). Then the signal envelope alignment of each channel is performed in the FrFT domain by using the characteristics of FrFT, so as to realize the multi-channel signal accumulation. Finally, the transmitter is accurately localized by the two-dimensional position search. Simulation results show that the proposed method outperforms the two-step method and DPD method in low SNR.

When detecting high-speed targets, the strong spoofing of range deception interference (RDI) can lead to failure of true target detection. Fortunately, the fusion processing provides an effective solution to this problem. In this paper, the target echo model under RDI conditions is established based on the range history model for multistatic radar system. On this basis, we propose an effective RDI recognition and target detection method based on coherent fusion processing. Specifically, the method firstly realizes the coherent fusion of single-channel echo by Radon Fourier transform (RFT). Then, topology-based entropy circulation matching (TECM) is used to accomplish the acquisition of the matching positions about target and RDI in different channels. Finally, the matching position is processed by elliptic positioning (EP) to realize the recognition of RDI and target. Simulation experiments verify the effectiveness of the method.

Ensuring the safety and security of our oceans demands a comprehensive Maritime Situational Awareness (MSA) strategy. However, this task has several challenges, including using multi-modal data, data sharing among agencies, privacy concerns, and bandwidth limitations. To address these challenges, this research introduced FedRS-Net. FedRS-Net is a federated deep learning framework that trains multi-modal remote sensing data without exposing client data. The system employs a communication-efficient federated averaging algorithm and a novel convolutional neural network architecture called Redesigned Skip Connection. It integrates synthetic aperture radar (SAR) and optical satellite imageries to achieve remarkable results. Extensive experiments were conducted on the maritime vessel datasets, resulting in a testing accuracy of $99.8 \%$. Further, applying secure aggregation and momentum-based gradient compression reduced communication costs by $\mathbf{7 \%}$. FedRS-Net overcomes privacy concerns and facilitates agency collaboration by enabling collective maritime monitoring through decentralized data. This research provides a robust federated learning solution tailored for multi-modal remote sensing analytics applications.

This article addresses the multi-channel integration detection issue of high-speed maneuvering weak targets in airborne coherent multi-input multi-output (MIMO) radar. Coherent MIMO radar can significantly improve the detection performance through joint intra-channel and multi-channel fusion processing. Nevertheless, the range migration (RM) and Doppler frequency migration (DFM) are resulted from highspeed motion, and the envelope and phase differences among multi-channels are challenging. To address these limitations, we propose a multi-channel integration approach in generalized Radon-Fourier transform (GRFT) domain. First, the system and signal models are established. GRFT is utilized to accumulate intra-channel energy. Then, we construct a set of coupled equations, and estimate the target’s position, speed and acceleration with the Newton-Raphson algorithm and solving linear equations. Based on the estimated outputs, the envelope alignment and phase compensation functions are constructed to eliminate the differences across channels. After that, the multi-channel fusion is realized in GRFT domain. The superiority of the proposed approach is shown via simulations.

This paper considers the problem of maneuvering target tracking in the clutter environment and proposes a graph attention autoencoder tracking network. Initially, the proposed method constructs the graph by using historical track points and measurement data of the current frame, including the target measurement and clutters, as vertices. Then, the graph is fed into the designed graph attention autoencoder (GAE) network to learn the relationships among the historical trajectory and the measurements of the current frame. Finally, the GAE outputs the tracking result of the current frame. The experimental results highlight that the proposed GAE can outperform the classic maneuvering target tracking algorithms in the clutter environment with low and high maneuverability.

This paper considers the problem of line of sight (LOS) and non-line-of-sight (NLOS) targets localization in an L-shaped corner. Specifically, first, the propagation paths of the electromagnetic waves in an L-shaped corner are analyzed by the ray tracing technology. Then, a sparsity-based multipath model is formulated by regularizing the LOS target with sparsity norm and the NLOS targets with jointly sparse across different paths, respectively. After that, a proximal gradient-based iterative algorithm is proposed to tackle this optimization problem. Finally, the feasibility of simultaneously localizing NLOS and LOS targets is demonstrated through simulations.

Methods by radar spectrograms are critical technologies in the field of human activity recognition. However, most methods are limited by the single domain feature expression and the use of a single time-frequency (TF) analysis method in the TF domain. In this paper, we propose a multi-domain fusion human activity recognition network based on multi-resolution TF spectrograms and range spectrograms for human activity recognition. The attention mechanism is utilized to extract spectrograms with different features in the range and TF domain. In the TF domain, the intrinsically relevant features of the three types of spectrograms are extracted using the 3-D convolutional neural network (3DCNN). Then the bilinear pooling is used to fuse the features of two different domains so that the features of the activity are comprehensively characterized. Finally the fused multi-domain features are extracted using the 2-D convolutional neural network (2DCNN). The results of the ablation experiments verify the validity of the proposed model.

This paper considers the building layout reconstruction (BLR) problem based on compressive sensing (CS) framework in the impulsive noise environment. Specifically, first, a CS-based imaging model considering the extended characteristics of walls is established. To realize effective BLR under impulsive noise, we formulate an optimization problem integrated maximum complex correntropy criterion (MCCC) and sparsity constraint. Then, a proximal-gradient-based iterative algorithm is introduced to solve the optimization problem. Finally, simulations under Gaussian noise and impulsive noise validate the effectiveness of the proposed method.

Airborne bistatic radar faces two main challenges when operating in look-down mode. One challenge is the issue of range cell migration (RCM) and Doppler migration (DM) due to movement between target and radar, while another challenge is the non-stationary nature for clutter. For the sake of tackling these challenges, a combined approach using sub-aperture processing is given. The method involves compensating for clutter Doppler and implementing sliding window processing to achieve sub-aperture space-time processing and eliminate clutter. Subsequently, spatial-temporal filtering elements with consistent phase are combined and reorganized to create the reconstructed echo matrix. RCM correction and coherent integration within each sub-aperture are then achieved through aperture division. Finally, RCM/DM compensation is carried out across diverse sub-apertures to realize energy integration. The effectiveness for this methodology is confirmed through simulation and processing of measured data.

This paper addresses the design of Mismatch Filter (MMF) for Linear Frequency Modulation (LFM) waveform so as to suppress the high range sidelobe. Firstly, the sidelobe level energy of correlation function between LFM and a spectrum weighted MMF is formulated to minimize. To handle this problem, an iterative gradient optimization utilizing Polyak heavy ball method is developed. Finally, numerical simulation are performed to evaluate its effectiveness, revealing that the proposed methodology shares lower Peak Sidelobe Level Ratio (PSLR) and Integral Sidelobe Level Ratio (ISLR), smaller Signal Noise Ratio (SNR) loss, and weaker mainlobe widening in comparison with the traditional windows.

The closure phase quantifies phase inconsistency caused by scattering phenomena such as vegetation, roughness, and soil moisture variations. This study explores the scattering characteristics of the closure phase. We use the singular value decomposition (SVD) method to separate the scattering phase from the closure phase. Then, we investigate the scattering properties associated with the zero and non-zero closure phase through H/α polarization decomposition in cropland. The results show that the zero closure phase area primarily corresponds to surface scattering, with a low entropy area (64.28%). However, the non-zero closure phase area is linked to medium-to-high entropy levels, implying a more complex scattering process (54.67%). This study clarifies the physical scattering properties behind closure phase values in cropland and provides insight for applications.

There are two main challenges for radar detection of low altitude maneuvering target over the sea-surface. One is range migration (RM) and Doppler migration (DM) caused by the radial motion between target and radar affect the coherent integration (CI) performance. The other is that the heavy sea clutter affects the detection performance. To solve these problems, this paper presents a combined method for maneuvering target CI and clutter suppression via two-step network. The CV-HUNet is applied to extract the target energy trajectory, and then the motion parameters of the maneuvering target are estimated by polynomial fitting. And the coherent integration (CI) is completed by matched filtering process via the estimated parameters. Next, the CI data is input into the ResNet to obtain the classification results for target and clutter, and the clutter suppression is realized by weighting operation. Finally, the constant false alarm rate detection is carried out to achieve the detection result. The validity of this method is verified by the measured dataset.

In this paper, we consider the problem of multipath suppression for through-the-wall radar imaging (TWRI). Exploiting the spatial diversity characteristic of multiple-input-multiple-output (MIMO) radar, we present a novel framework integrated compressive sensing and spatial filtering to formulate a ghost-free image with high-resolution. Specifically, an array signal processing model that accounts for the direction of departure (DoD), direction of arrival (DoA) and time of arrival (ToA) is established, where the signal with unequal DoD and DoA is regarded as the multipath. For the purpose of computational efficiency, we present a two-step method to solve the proposed large-scale problem, where the alternating direction method of multiplier (ADMM) and Capon beamforming are employed. Finally, the performance of the proposed method is verified via simulations.

Gesture recognition in few-shot scenarios presents a significant challenge due to the scarcity of labeled data. In this work, we propose a novel end-to-end framework tailored for few-shot gesture recognition, addressing the limitations of current methods. A novel feature map generating method is proposed to leverage a greater number of dimensions in capturing gesture feature information and simplify the structure of network. Our approach also maximizes the utility of a limited set of real training samples by generating new virtual samples in two domains based on data augmentation, and employs a feature fusion strategy for comprehensive gesture characteristic extraction by using both Convolutional Neural Network (CNN) and Histogram of Oriented Gradients (HOG) to extract features. Extensive experimental results validate the efficacy of our proposed method, achieving a final accuracy of 85.26%, exhibiting a remarkable 35% improvement over the baseline, thereby confirming the effectiveness of our work in the challenging few-shot gesture recognition task.

As a 3-D point cloud has the ability to present the contour of an object clearly, it provides more spatial information for person identification (PI) task. Aiming at the improvements on the quality of point cloud and distribution of features, an innovative treatment method for point cloud and a novel network structure are investigated in this article. First, spatiotemporal feature of point cloud is enhanced by implementing dual-stage density-based spatial clustering of applications with noise (DST-DBSCAN) method, which can filter most invalid points and decrease the sparsity of point cloud. After that, the optimized point cloud is input into neural network, which contains three parts for feature extraction, classification and feature optimization. Specifically, PointNet++ is adopted to extract features and realize PI recognition. In addition, an adversarial network is designed for optimizing feature distribution of point clouds by encouraging the feature extractor of PointNet++ to generate features of the same person as similar as possible. Experimental results demonstrate that the proposed method can improve the accuracy by 3.77% than original PointNet++ network with raw data.

To tackle the issue of space-time adaptive processing (STAP) performance degradation caused by inaccurate estimation of the clutter covariance matrix (CCM) with limited sample support, a knowledge-aided (KA) CCM estimation algorithm based on the symmetric alternating direction method of multiplier (KA-SADMM) is proposed. The CCM estimation problem is constructed based on the knowledge of persymmetric structure, the low-rank structure, and the prior covariance matrix, and the solution to the resulting problem is derived. Moreover, the contraction property of the sequence generated by KA-SADMM with respect to the solution set of the estimation problem is analyzed. With limited training samples, the simulation results show that the proposed algorithm improves the STAP performance over other similar algorithms by at least about 0.2 dB when the prior knowledge is accurate and by at least 0.3 dB when the prior knowledge is inaccurate.

Excellent performance in supervised learning-based radar signal modulation recognition (RSMR) techniques relies on the quantity and quality of labeled datasets, while the high cost and difficulty involved in analyzing and labeling radar signal samples limits its development. An RSMR algorithm using self-supervised contrastive learning (SSCL) methodology is proposed to address this issue. Specifically, within the classical contrastive learning (CL) framework MoCo V2, a customized data augmentation method is devised to capture time-frequency features of the radar signal. In addition, the feature extraction network ResNet50 is improved by decoupling spatial and channel filters, resulting in greater sensitivity to the time-frequency features. To enhance the recognition accuracy, two loss functions, alignment and uniformity, are used in place of the info noise contrastive estimation (InfoNCE) loss, and both loss functions are optimized directly. The recognition accuracy of the proposed method can reach 97.66% at a signal-to-noise ratio (SNR) of 4 dB.

In this letter, we provide the target signal enhancement method based on deep learning for weak target detection. First, the proposed method fully considers the nature characteristic of radar complex echoes and exploits the complex-valued neural networks. Then, the architecture of weak target enhancement complex-valued generative adversarial network (WTE-CGAN) is proposed. More specifically, the generator loss function of generative adversarial network (GAN) is modified, which can be used to reflect the difference between the generated target signal by the generator and the label signal. To keep the training stability of the proposed method, a gradient penalty factor is randomly added to every sample, which embodies the loss function of discriminator. Finally, simulation and measured experiments are given to demonstrate the effectiveness of the proposed method compared with other methods, and it has a significant signal enhancement effect on weak targets.

This letter considers the problem of target detection and tracking with millimeter-wave (mm-wave) frequency-modulated continuous-wave (FMCW) radar and proposes a tracking method via learning the displacement of the target in successive frames. First, a convolutional neural network (CNN), namely, detection and displacement network (DDNet), is trained to predict the target positions at current frame and the displacement relative to last frame simultaneously with the range-azimuth (RA) spectrum in two adjacent frames. Then, the detections are associated through the predicted displacement to construct the trajectories. Finally, the effectiveness of the proposed method is validated on both simulated and real-world datasets.

In the field of through-the-wall radar imaging , existing compressive sensing (CS) methods mainly concentrate on deriving indoor targets image while overlooking the reconstruction of building layout image. In this paper, we focus on the problem of utilizing CS for building layout reconstruction (BLR) in wall reverberation environment. Specifically, first, by incorporating the characteristics of building layout, an extended target CS imaging model in wall reverberation environment is established. Then, an extended-target-based group block CS (ET-GBCS) algorithm based on the alternating direction multiplier method is proposed to accurately reconstruct the building layout. After obtaining the reconstructed result of each view, the total variation minimization method is used to process the multi-view fusion result for building layout edge preservation and image noise removal. Finally, the effectiveness of the proposed algorithm is verified by electromagnetic simulations.

The pursuit of high-quality building layout images is a key objective in radio tomographic imaging (RTI) as it provides essential information for precise indoor target localization. This study addresses the challenge of tomographic imaging for three-dimensional (3D) building layout, introducing a tensor-based enhancement imaging method. Specifically, first, the linear tomographic model is built by considering the relationship between the time delay of the transmissive signal and unknown region. By solving the tomographic model, the initial spatial map can be derived, and it is characterized as a three-order tensor, encapsulating the spatial attributes of the building. In the proposed enhanced imaging method, it leverages the spatial correlations, smoothness, and adaptive group sparsity properties inherent in 3D building layouts, and embeds those prior knowledge into the tensor-based optimization framework, which not only enhances reconstruction accuracy but also suppresses the striping artifacts. Numerical simulations and experiments are conducted to validate the proposed algorithm, with comparisons made against state-of-the-art methods. The results consistently demonstrate a substantial improvement in the quality of building layout image, which underscores its high potential and applicability within the field of radio tomography.

Building layout tomographic imaging (BLTI) is a nascent technology that effectively addresses issues, such as wall cavities and offsets, encountered in traditional synthetic aperture radar (SAR) imaging. Nevertheless, the current single-input single-output (SISO) imaging system is susceptible to multipath signals (MPSs) interference, leading to issues, such as artifacts and distortions in the imaging results. In this article, a tomographic scanning system based on multiple-input multiple-output (MIMO) ultrawideband (UWB) radar is designed, which can accurately acquire the delay of direct-path signals (DPSs) delay by the similarities of DPS across different channels. Next, a linear tomographic projection model is established according to the relationship between DPS delay and the scene to be imaged. Furthermore, a joint sparse imaging algorithm with adaptive projection matrix modification (JS-APMM) is proposed. It takes into consideration the prior information of building layout and material to improve imaging quality under limited data. Finally, simulation and experimental results demonstrate the superiority of the proposed method over the single-input single-output (SISO) imaging system and state-of-the-art compressed sensing algorithms.

Non-line-of-sight (NLOS) moving target imaging is an emerging and challenging technology with potential applications in autonomous driving, security detection, disaster response, and more. In this article, a novel algorithm dubbed NLOS detail recovery via alternating direction method of multipliers (NDR-ADMMs) is proposed for NLOS moving target imaging. In our scheme, the static clutter filter (SCF) we proposed is utilized for NLOS clutter suppression, which facilitates hidden motion target echo extraction. To address the sparsity of echoes caused by scene complexity and target motion, we introduce a regularization constraint termed detail-aware regularization (DAR), which enhances details and suppresses noise in NLOS scenes by incorporating information from neighboring cells and expanding the receptive field of the image. Then, we propose the NDR-ADMM that combines DAR,$\ell _{1}$-norm, and ADMM to reconstruct high-resolution NLOS moving target images. Further, the corresponding fast version, NDR-ADMM+, is derived by mapping the NDR-ADMM to the adaptive parameter learning network for improving robustness and convergence. Finally, the proposed NDR-ADMM and NDR-ADMM+ are verified by simulated data and measured data we collected via millimeter-wave (MMW) radar in various NLOS scenarios. Compared to other state-of-the-art methods, NDR-ADMM+ demonstrates superior performance, robustness, and noise immunity, with NDR-ADMM following closely behind. This is attributed to DAR’s ability to capture details and suppress interference. Additionally, NDR-ADMM+ and AF-AMPnet offer the fastest processing speeds.

Building layout reconstruction (BLR) is a prominent research topic in the field of through-the-wall radar (TWR) and wireless perception. Inspired by computed tomography (CT), the transmit–receive separated dual-bistatic radar system utilizes electromagnetic (EM) wave transmission signals to perform BLR. However, existing researches significantly rely on signal frequency bandwidth resources. Moreover, state-of-the-art researches only estimate the time delay (TD) information, thereby posing challenges in precisely discriminating between the direct path (DP) and multipaths. This article refines the sparse signal reconstruction-based angle of arrival (AOA) and TD super-resolution estimate algorithms under the condition of restricted broadband array signals. In accordance with this, the present study proposes a DP-identifying criterion with the assistance of AOA and obtains high-accuracy DPTD estimation. Furthermore, with the high-accuracy DPTD estimation, this article proposes a common material permittivities-based iterative multimaterial BLR algorithm. The final numerical simulations and EM simulations verify the effectiveness of the proposed super-resolution algorithm and the improvement of multimaterial BLR.

This paper investigates integrated waveform design for radar systems to simultaneously achieve a low probability of intercept (LPI) by an adversary electronic support measure (ESM) system while maintaining communications with other radar nodes. A reconfigurable intelligent surface (RIS) is exploited and jointly designed to enhance the achievable performance of the whole system. LPI detection based on the ESM’s feature analysis on the radar waveform is achieved under the constraints of a desired signal-to-interference-and-noise ratio for the radar detection and a desired signal-to-noise ratio-based quality of service for the communication channels. To deal with the resulting non-convex optimization problem, a suboptimal composite algorithm with polynomial complexity is proposed. The initial feasible solution of the algorithm is efficiently obtained through an asymptotic optimization problem. Finally, the complexity of the proposed algorithm is analyzed. Simulation results including comparisons with baselines highlight the effectiveness of the proposed scheme.

This paper deals with the integrated signal design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple Input Multiple Output (MIMO) system. Inter-pulse modulation strategy via jointing the information embedding subpulses positions selections and differential phase modulation is proposed for communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, constant envelope, communication modulation and mainlobe width constraints are forced. To tackle the resulting nonconvex and NP-hard optimization problem, the Linearized Approximation Alternating Direction Penalty Method (LA-ADPM) is proposed. Finally, numerical results demonstrate the designed integrated signal is capable of realizing high data rate communication and ensuring acceptable radar performance.

In the field of ultra-wideband (UWB) multi-input multi-output (MIMO) imaging, it is difficult to recognize targets, especially weak targets, in the high grating lobes environment. To solve this problem, in this paper, an adaptive sub-band sub-aperture coherence factor (ASBSA-CF) algorithm is proposed based on sub-band sub-aperture (SBSA) image data. First, we establish the objective function by using the maximum inter-class variance (OTSU) method. Then, particle swarm optimization (PSO) algorithm is applied to adaptively choose the number of sub-bands and sub-apertures of SBSA image data. Finally, simulation verifies that ASBSA-CF can optimally achieve the compromise between weak targets and grating lobes suppression.

Under the large time-bandwidth product condition, the detection of hypersonic maneuvering target usually confronts two main issues: the scale effect (SE) and range migration (RM)/Doppler migration (DM). To address these issues, this paper considers the intrapulse and interpulse motions simultaneously to establish the echo model of hypersonic target with acceleration firstly. Then, the matched filtering-scaled modified location rotation transform (MF-SMLRT) method is proposed to jointly eliminate the SE and RM/DM, which are realized by searching target’s rotation angle , initial range cell and acceleration. The proposed method shows better pulse compression (PC), integration and detection performance than the conventional narrow-band long-time coherent integration (CI) methods and existed CI methods applicable to large time-bandwidth products (LTBP) condition. Finally, simulated results are given to certify the validity of the proposed method.

This letter proposes a novel window function design to synthesize asymmetric beampatterns while minimizing the gain loss for clutter/interference suppression as required in practical applications, such as airborne phased array radar (APAR). Compared with beampattern synthesis in terms of designing the weight vector directly, the proposed method can achieve asymmetric sidelobes for multiple steering directions resort to designing one window while circumventing repetitive computations. To tackle the resultant non-convex optimization problem, an iterative alternating optimization (IAO) algorithm is introduced where each iteration involves multiple subproblems that can be solved via either convex approximation (CA) approach or closed-form solutions. Finally, numerical simulations are provided to verify the effectiveness of the proposed method comparing with the classical adjustable window functions (CAWFs).

Existing works on sub-pulse-based frequency modulated continuous wave (FMCW) dual-functional radar-communication (DFRC) lack of a complete analysis of the radar processing links and an effective solution to the radar performance deterioration problem. This letter conducts a complete signal modeling for the FMCW-DFRC signal processing. By compensating the changes generated by data modulation, a radar processing scheme based on dual path compensation and splicing (DPCS) is proposed to achieve the same radar performance of an unmodulated FMCW. Numerical results show that compared with the existing FMCW-DFRC works, DPCS achieves a sidelobe suppression enhancement of about 27 dB

Ghost targets resulting from the reflection of electromagnetic waves by stationary objects are a long-standing issue. Currently, the majority of techniques for addressing multipath ghost targets involve either prior awareness of the environment or cooperation between multiple radars, which has its drawbacks. To address the problem with a single radar, this paper proposes a multipath ghost recognition method based on the difference between direction of departure (DOD) and direction of arrival (DOA) for sparse multiple input multiple output (MIMO) radar that does not require prior building information. This method first achieves DOD and DOA estimation of the signal through minimum variance distortionless response (MVDR). Then, a grating lobe suppression method for sparse MIMO radar in multipath environments is proposed based on the distribution characteristics of DOD-DOA spectral peaks of multipath signals. In that case, the recognition of1-ordermultipath is completed by using the features of two types1-ordermultipath DOD and DOA being unequal. After that, the recognition accuracy of1-orderand non-combined multipath signals is improved by accumulating multiple frames. On this basis, the linear relationship among the distances corresponding to0-ordermultipath,1-ordermultipath and2-ordermultipath, is utilized to complete the recognition of2-ordermultipath signal. Finally, several experiments are carried out to verify the effectiveness of the proposed method.

In this paper, we consider a high-accuracy and low-complexity method for recognizing human activities behind wall. As the amount of information conveyed by data representation directly affects the recognition accuracy of the network, we construct the three-dimensional (3D) complex-valued feature for human activity recognition (HAR). In light of high network complexity introduced by 3D complex-valued data, we devise a low time and space complexity network named Convolutional Gated Recurrent Unit based on region-vectorization (RV-ConvGRU). Keystone Transform is utilized to process the radar echo and generate 3D complex-valued Range-Time-Doppler (RTD) data first, which provides high-frequency resolution and abundant feature information. Then, the real and imaginary parts of the complex-valued RTD are separately fed into a feature extraction module to comprehensively extract their respective features. Specifically, the real or imaginary part of the RTD is divided into multiple regions, which are then converted into regional vectors and reordered as channels to reduce the time and space complexity of the subsequent network. The reconfigured features are then input into the Convolutional Gated Recurrent Unit (ConvGRU) to extract global and temporal features, with the channel attention mechanism for feature selecting. The features of the real and imaginary parts are fused and then classified by the classifier finally. The experiments verify that the proposed method is effective, achieving the highest recognition accuracy of 99.23% with an input sequence of 1.44 seconds.

This article explores the coherent integration and parameter estimation problem for high-speed target detection using bistatic multiple-input multiple-output (MIMO) radar. Unlike the traditional intrachannel coherent integration that could only focus the target’s energy during the multipulse observation, the bistatic MIMO radar implements joint intrachannel integration and interchannel integration to improve detection performance. However, its difficulties lie in that the high-speed motion results in the range migration (RM) in the intrachannel and the phase and envelope differences among multichannel. To solve these problems, we propose a multichannel coherent integration methodology in the radon Fourier transform (RFT) domain for the bistatic MIMO radar. First, we apply the RFT to integrate the target energy in the intrachannel. Then, the multichannel coherent accumulation is performed by the design of the phase compensation function and envelope alignment function. Finally, based on the relationship between the integration peak’s location and target parameters, a Broyden-like method and linear equation solving are, respectively, presented to estimate the target’s position and velocity. The effectiveness of the proposed method is assessed via simulation experiments and real-measured data. It is further noted that the proposed method can detect targets at low signal-to-noise ratios (SNRs) which are not attainable using state-of-the-art methods.

Localization of non-line-of-sight (NLOS) targets in the complex urban environment have attracted significant attention in recent years. However, the requirement for precise prior information about the environment is idealistic. It is challenging to know the environmental information in the blind area of vision in advance of practical applications. This paper proposes a joint estimation algorithm for building layout and target position in the L-shaped scene without any prior information. Specifically, a round-trip multipath propagation model is first developed for the cases of diffraction and multiple reflections. Then, the received echo signal is preprocessed with moving target identification (MTI), back-projection (BP) imaging, and image segmentation. In addition, the target points, which are screened by geometric association, are further matched and estimated by the multipath ghost’s hypothesis method, thus realizing the joint perceptual estimation of the building layout and the target position. Finally, electromagnetic (EM) simulations and experimental measurements are used to validate the effectiveness of the proposed algorithm.

In this article, the watermarking framework for electromagnetic systems is established with nonblind, semiblind, and blind watermarking demodulation processes mitigated to applications like radar detection, synchronization, integrated dual function, etc. Desired for similar advantages and trade-offs of watermarking technology, the dual function radar and communication (DFRC) system is specifically concerned for covert communication and low possibility of interception (LPI) radar sensing. In this respect, we propose a novel DFRC waveform design method where peak sidelobe level (PSL) of autocorrelation function (ACF) is considered as the figure of merit with information embedded via discrete Fourier transform (DFT) watermarking strategy. Meanwhile, the peak-to-average ratio (PAR) and energy constraints are forced to ensure compatibility with current hardware technique. To handle the resulting NP-hard design problems, the proximal method of multipliers (PMM) is employed with the overall computational burden linear with the amount of information per pulse and quadratic with respect to the code length. Finally, numerical and experimental results are provided to evaluate the effectiveness of the proposed DFRC waveform design scheme with application in joint synthetic aperture radar (SAR) imaging and communication.

Airborne radar mainly confronts two problems for marine moving target detection. One the one hand, the relative velocity and acceleration between airborne radar and target cause range cell migration (RCM) and Doppler frequency spread (DFS), degrading the CI performance. On the other hand, heavy sea-clutter influences the focusing/detection results. To solve these problems, this paper proposes a novel fast method based on time slice location rotation transform (TSLRT), matched filtering processing (MFP) and singular value decomposition (SVD), i.e., TSLRT-MFP-SVD. This method combines the ideas of time slices division and echo subspace decomposition to realize valid CI and sea-clutter restrain. Real-data processing prove the validity of the proposed method.

Human motion recognition based on ultra-wideband through-the-wall radar (UWB TWR) (a radar whose fractional bandwidth of the radar transmitted signal is bigger than 0.25) is faced with the problems of too few samples and the limitation of perspective. In this letter, we propose a multiradar cooperative human motion recognition model based on transfer learning and ensemble learning. Specifically, a ResNeXt network model based on transfer learning is first proposed to deal with the problem of too few samples. The model is pretrained on the public ImageNet database, and then it is transferred to the task of human motion recognition based on multiradar. Compared with a typical convolutional neural network from scratch, the ResNeXt network model based on transfer learning requires shorter epochs and achieves higher accuracy. Then, to solve the problem of model accuracy decline caused by the limitation of perspective, a multiradar human motion recognition model based on ensemble learning is proposed. Experimental results show that compared with the fusion model based on single-view radar, the recognition accuracy of network based on ensemble learning can be higher.

Through-wall human motion recognition is suffered from the problems of too few samples and too large model parameters. In this letter, we propose a multi-domain fusion through-the-wall radar (TWR) human motion recognition model based on lightweight network and transfer learning. Specifically, in order to make full use of the target information, a multiple parallel feature pyramid network (FPN) is first proposed to extract the detailed feature information from the time–frequency map and range profile. After that, a lightweight network based on the MobileNetV3 network and transfer learning is proposed. The MobileNetV3 model is pre-trained on the public ImageNet database. To ensure the performance of transfer learning, a heterogeneous migration learning algorithm is used to cross-domain transform the obtained time–frequency map and range profile. Experimental results show that the proposed model has a better performance in accuracy, model size, training time, and robustness compared with the existing methods. It also has the potential to embed portable radar, which has important research value for the application of radar in real life.

This letter studies the problem of ultrawideband (UWB) tomographic imaging for unknown building layouts where the multipath-rich condition is considered. Specifically, first, the multiple propagation paths of the UWB signal are analyzed, and a delay estimation algorithm is proposed to estimate the direct path (DP) delay from the multipath signal. Then, a tomographic projection model is established by mapping the relationship between the delay of the DP and the relative permittivity of the unknown layout. Besides, a modified total variation method is developed to reconstruct the building layout, which shows significant performance in preserving the edges of the structure. Finally, the effectiveness of the proposed algorithm is verified using both simulated and real data.

Accurate sensing of the main-lobe active deception jamming is critical for radar anti-jamming and extended target detection in complex electromagnetic environment. This letter therefore deals with the problem of multiple active deception jamming recognition in extended target settings. A residual convolutional neural network with attention mechanism-based radar active deception jamming recognition algorithm is proposed leveraging a hybrid model to capture much rich features through multi-domain feature fusion. The proposed method can outperform state-of-the-art methods in terms of recognition accuracy, model size, and convergence speed. Experimental results demonstrate its effectiveness and robustness.

This letter considers the problem of human activity recognition (HAR) behind the walls using ultra-wideband (UWB) radar. The graph convolutional network (GCN)-enhanced multi-domain fusion network (GMFN) is proposed to improve the recognition performance utilizing the complementarity of the multi-domain features. Specifically, firstly, a multi-branch convolutional neural network (CNN) is proposed to extract the multi-domain features from the range, time-frequency, and range-Doppler domain. Then the multi-domain features are constructed as a graph, and the GCN is employed to fuse the multi-domain features on the graph. Finally, HAR is implemented in the form of the graph classification. The experimental results on the real data show that the proposed GMFN achieves better performance than the state-of-the-art multi-domain fusion HAR methods.

This letter considers the problem of the target detection in the nonhomogeneous sea clutter environment, and proposes the complex-valued U-Net based clutter suppression method. Specifically, firstly, the complex signal features of radar echo sequences are extracted by developing the complex-valued convolutional blocks. Secondly, the complex multi-level features are fused, by employing the up-down sampling structure and skip connections, to suppress nonhomogeneous sea clutter. Further, the false alarm controllable detector is designed to detect the targets. Finally, the performance of the proposed method is evaluated via real data. The results show that it has a higher detection probability compared with the real-valued U-Net.

In this letter, the correlation structure inherent in the received data is exploited to improve detection performance for sea-surface small floating targets in short observation time. Three detectors are devised resorting to the eigenvalues of covariance matrix that are versatile statistics reflecting the signal correlation. Specifically, the proposed detectors respectively exploit the maximum eigenvalue to arithmetic mean (MAM) of all eigenvalues, the maximum eigenvalue to geometric mean (MGM) of all eigenvalues, and the maximum eigenvalue to minimum eigenvalue (MME) to form test statistics. At the analysis stage, the three-parameter Burr function is exploited to approximate the statistical distributions of the test statistics under null hypothesis and alternative hypothesis. Besides, the analytic expressions of false alarm probability, detection probability, and thresholds of the proposed detectors are derived. Finally, simulation results on real sea clutter show that the proposed detectors provide effective solutions to the problem of target detection in sea clutter.

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and data-driven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called$\alpha $-sum and$\alpha $-product of maximum and minimum eigenvalues ($\alpha $-SMME,$\alpha $-PMME) are further designed, in which$\alpha $is a weight coefficient. Furthermore, the theoretical performance analysis of the$\alpha $-SMME and$\alpha $-PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector.

Building layout reconstruction (BLR) is an important topic in the field of through-the-wall imaging. Traditionally, reflective signals are commonly used to generate an accurate building map. However, due to the inherent features of electromagnetic waves, the reconstructed walls will inevitably suffer from problems such as deviation and cavities. Alternatively, as an extension of computed tomography, the transmissive signals can be exploited for BLR with high efficiency, but its performance degrades seriously when the sampling views are sparse. In this paper, to fully combine the superiority of the two types of implementations of BLR, we proposed a hybrid imaging framework by jointly exploiting the reflective and transmissive signals to retrieve the unknown layout. Specifically, first, the time delay of the transmissive signal will be estimated and used to reconstruct the spatial tomographic map. Then, a series of reflected echoes sampled by different routes will be compensated iteratively and used to generate the back-projection image. Finally, we fuse the two images generated by different types of signals using the feature-level detector. Both simulated and experimental results reveal that the proposed imaging framework can yield better performance compared with the image derived from single-type signals.

Non-line-of-sight (NLOS) detection is an enduring topic as it provides a powerful tool to monitor visually blocked areas. Currently, the NLOS detection requires precise prior knowledge of building layout, which limits its further applications in practice. In this paper, we consider the problem of joint estimation of building layout and target location in the NLOS scenario by exploiting multipath returns. Specifically, first, the building layout is simplified into combined linear equations with unknown parameters. In this way, we establish a parametrized multipath propagation model in the multiple targets NLOS scenario for the multiple-input-multiple-output (MIMO) radar, which is used in the image reconstruction and layout estimation problem. Then, a shape-remodeling group sparse constraint algorithm is proposed and combined with the particle swarm optimization method to simultaneously reconstruct the unknown layout and targets. Compared to the conventional compressed sensing-based methods, the proposed method integrates the basic structural characteristics and sparsity prior of the NLOS image to improve the stability of the solution. Finally, the performance of the proposed method is verified with numerical and experimental results.

Ambiguity clutter for airborne radar system is usually caused by uniform pulse repetition interval (UPRI) waveform, which significantly degrades target detection and location performance. To address this issue, this paper proposes an ambiguity clutter suppression method resorting to pseudorandom pulse repetition interval (PrPRI) waveform. First, airborne radar clutter model accounting for multiple range rings with PrPRI waveform is developed. Next, a non-uniform coherent processing framework is introduced to eliminate the clutter folded in the range and Doppler domain. In particular, the reasons for the enlargement of the area free of clutter corresponding of PrPRI mode are analyzed, as well as the maximum unambiguous Doppler frequency, maximum unambiguous range and design principles for PRI are derived. Finally, numerical examples are designed to verify that the non-uniform coherent processing framework can enlarge the clutter-free area and achieve the unambiguity target detection and location.

Nonline-of-sight (NLOS) radar imaging is a novel technique that can inverse the scattering characteristics of targets in the NLOS area, which has been one of the hot pots of radar imaging field. However, the existing NLOS radar mainly focuses on 1-D or 2-D imaging, which inevitably suffers from the geometric loss of real 3-D scenes, and its applications are restricted in the urban environment. In this article, we propose an NLOS radar 3-D imaging model and method for looking around corner (LAC) situation by multi-input–multioutput (MIMO) millimeter-wave (mmW) array antennas. In this scheme, first, the model of NLOS radar 3-D imaging with mmW MIMO antennas is established and the multipath scattering of targets with this model is analyzed. Then, the theoretical resolution of LAC 3-D imaging is derived and discussed. Second, exploiting the three bounces of LAC and extraction of linear structure, an effective imaging algorithm with mirror projection theory and Radon transform, dubbed as mirror symmetry backprojection (MSBP), is proposed for 3-D image focusing. Moreover, to suppress the uncertainties of phase caused by both LAC and system error, the minimum entropy principle is introduced to MSBP. Finally, an NLOS 3-D imaging system with 77-GHz mmW MIMO radio frequency module and 2-D rails is developed. Different types of targets, such as metal balls and ornaments, are tested in LAC. The results demonstrate that our NLOS technique can not only provide a high-quality 3-D focusing of the hidden targets but also extract positions of targets without prior knowledge of the NLOS area.

Recently, wireless communication signals are not only used for information transmission but other possibilities are being developed, such as human body state estimation, wireless sensing, etc. In this article, the radio tomographic reconstruction of building layout and interior object is considered only utilizing the received signal strength indication (RSSI) of commercial WiFi signals. Specifically, first, an electromagnetic transmission energy attenuation model is introduced, which considers the case for scanning the entire unknown area using two wireless nodes. Then, we propose a novel algebraic iterative reconstruction method, which takes into account the constraint of prior knowledge and designs a more reasonable initial iteration factor. Finally, a variety of scenarios with different structures is built, and the reconstruction of the multimedia scenario is achieved for the first time with RSSI information. The simulation and experimental results show that the proposed method has better performance than current state-of-the-art algorithms in both the single-medium scenario and multimedia scenario.

This letter mainly deals with the problem of counting moving human targets in an enclosed building space for through-the-wall radar. Specifically, a typical deep convolutional neural network, namely, residual neural network (ResNet), is designed to identify the line-like texture information associated with the target number from the blurred range-time images of a single-channel stepped-frequency continuous-wave (SFCW) radar. Experiments demonstrate that the ResNet-based counting algorithm achieves an accuracy of 91.54% for one to six human targets, and the accuracy rises to 97.12% when only counting one to three humans, even under conditions of wall penetration degradation, limited spatial resolution, heavy multipath clutters, and target-to-target occlusion. The achieved number of information of moving human targets not only contributes directly to the situation assessment behind the wall but also can act as the prior information to promote further target detection.

Shape variance of target images, image overlapping for adjacent targets, and weak scattering target detection are critical challenges of human target detection for through-the-wall radar imaging. In this letter, an adaptive target detection method is proposed based on fully convolutional network (FCN). The downsampling-upsampling structure is employed to extract multiscale features. The attention mechanism is integrated with the FCN for weak scattering target detection. Exploiting both the intensity and geometrical features of the target image, the proposed algorithm could overcome the abovementioned challenges and achieve better detection performance compared with the state-of-the-art methods. The proposed algorithm is evaluated via simulation and experimental tests.

This paper is focused on the joint design of transmit waveform and receive filter in the presence of deceptive interference. In particular, a design criterion incorporating a weighted sum of integrate sidelobe levels (ISLs) with respect to the receive filter and transmit waveform as well as the receive filter and interference waveform is developed to minimize along with interference nulling, constant modulus and signal-to-noise ratio (SNR) restrictions. To tackle the resulting non-convex optimization problem, a new decoupled alternating direction penalty method (DCADPM) is proposed based on the ADPM framework. In each iteration, it converts the considered problem into multiple tractable subproblems with closed-form solutions via introducing auxiliary variables. Finally, numerical results are provided to demonstrate the effectiveness of the proposed methodology, highlighting that it is capable of rejecting many kinds of deceptive interferences.

In this letter, we consider the problem of human target detecting and tracking, exploiting small-aperture through-wall imaging radar. We build a novel target model considering both the statistical and the geometrical information of the target image. A scale-adaptive target tracking method is proposed to track the scale and orientation variant human targets based on the mean-shift tracking framework, where the image moments are exploited to estimate the scale and orientation of the target image dynamically. Finally, the proposed algorithm is evaluated by simulations and experimental results.

Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance structure. The resulting constrained optimization problem is solved globally resorting to the Dykstra' projection framework. Each step of the procedure involves the solution of two convex sub-problems, whose minimizers are available in closed form. Simulation results related to typical radar environments highlight the effectiveness of the devised method.

This letter deals with the robust joint design of radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case Signal-to-Interference-plus-Noise-Ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

This paper deals with constant modulus waveform design in spectrally dense environments assuming a discrete phase code alphabet. The goal is to optimize the radar detection performance while rigorously controlling the injected interference energy within each shared band and enforcing a similarity constraint to manage some relevant signal features. To tackle the resulting NP-hard optimization problem, an iterative procedure characterized by a polynomial computational complexity, is introduced leveraging the coordinate descent method. Numerical results are provided to show the effectiveness of the technique in terms of detection performance, spectral shape and autocorrelation features.

This paper solves the through-wall imaging (TWI) problem with a narrow-band system, and proposes an adaptive TWI method based on data fusion of multiple scan paths. First, we use a Wentzel-Kramers-Brillouin-based (WKB-based) approximation to model the interaction of the transmitted wave with the unknown area. Then we use Radon inverse transform to reconstruct the image from the received signal strength data of different paths. Furthermore, we evaluate the impact of scan paths on imaging. Finally, finite-difference time-domain (FDTD) simulation results demonstrate the validity of proposed method.

This paper deals with the tracking problems for multiple human targets hidden behind the wall using through-wall imaging radar (TWIR). We propose a robust tracking algorithm in image domain, combining mean-shift algorithm with Kalman filter. Comparing with the traditional mean-shift algorithm, the proposed algorithm has a greater performance in multiple human targets tracking, especially considering the case of the temporary loss of target. Real data validates the robustness of the proposed algorithm.

This paper considers the location problem for Moving targets behind a corner. Exploiting multi-path and the algorithm based on phase comparison among the multiple channels can obtain the position of the target behind a corner. To localize the moving target, a scanning radar system with multiple channels is suggested. The false target range can be achieved by the fast Fourier transform(FFT) technique. In addition, the false target azimuth is derived via exploiting the phase differences between the return signals among the multiple channels. Due to false targets and real targets are geometric symmetry, true targets can be localized by the radar system. Finally the experiment results validate this method, and demonstrate the effectiveness.

In this letter, we consider the problem of multipath ghost suppression for through-the-wall imaging radar. Exploiting the fact that these locations of the multipath ghosts depend on while the target location is independent of the array configuration, we present a novel framework via array rotating to eliminate the multipath ghosts. Specifically, we first rotate the array with multiple different array rotation angles. Then, multiple images are derived using back-projection imaging algorithm. Finally, the incoherent arithmetic fusion method is applied to yield a ghost-free image. The proposed approach has two advantages for multipath ghost suppression. First is the simplicity of the operation, and the second is that it will not be affected by the incorrect wall parameters. Ghost suppression performance of the proposed approach is evaluated via numerical simulations.

In this paper, under the situation of multiple interference regions, an optimal antenna placement problem for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple interference regions, we solve the antenna placement problem by utilizing antenna placement method based on Multi-Objective Particle Swarm Optimization (MOPSO). However, it is not clear when to stop the iteration for which no knowledge about the optimum result is available. Hence, computational resource may be wasted over iterations. Nevertheless, time and computational resource is limited in real application. Therefore, to obtain the optimal placement result with limited time and computational resource, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the optimization process efficiently when the optimal antenna placement algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.

This paper considers the location and tracking problem for the indoor and outdoor targets with the single input multiple output (SIMO) radar. An effective algorithm based on phase comparison is presented to derive the target azimuth by exploiting the phase differences between the return signals among the multiple channels. In addition, the target range is derived via employing the fast Fourier transform (FFT) technique. Combined with the azimuth achieved, this method can be applied to accurately locate and track the moving targets whatever indoors or outdoors. Finally, the experiment results validate this method, and demonstrate the effectiveness.

This study addresses the coherent accumulation problem for detecting a manoeuvring target with complex motions, where range migration (RM) [i.e. range walk (RW) and range curvature (RC)] and Doppler frequency migration (DFM) occur during the coherent integration time. An efficient approach based on generalised keystone transform (GKT), radon transform (RT) and generalised dechirp process (GDP), i.e. GKT‐RT‐GDP, is presented to eliminate the RM and realise the coherent accumulation. More specifically, the GKT operation is first employed to remove the RC. Then, the RT is applied to estimate the trajectory slope for RW correction and velocity estimation. After that, GDP is introduced to obtain the estimations of target's acceleration and acceleration rate motion. Thereafter, the DFM caused by target's high‐order motions can be compensated and then the coherent integration can be realised via Fourier transform. The advantage of the presented algorithm is that it can obtain a good balance between the computation cost and the detection performance, in comparison with the existing coherent integration algorithms.

This letter focuses on the local ambiguity function shaping through unimodular sequence design. An accelerated iterative sequential optimization (AISO) algorithm is proposed to minimize the average value of the weighted integrated sidelobe level (WISL) over specific Doppler bins and range bins of interest. We evaluate the effectiveness of the AISO algorithm in terms of its achieved WISL and computational complexity in comparison with the gradient method via numerical examples. The capability of using the design to detect high-speed targets is also evaluated.

This paper considers robust vehicle logo recognition (without aiming at accurate location) for intelligent transportation systems. We propose a recognition-before-location framework for multi-scale vehicle logos which exploits a directional SIFT flow parsing method. We extract dense SIFT descriptors of different standard vehicle logos. An improved matching method is proposed to obtain a directional SIFT flow from standard logo models for vehicle images. Our vehicle logo recognition algorithm is based on dense SIFT matching energy and SIFT flow consistency. We verify the accuracy of vehicle logo recognition and the robustness for multi-scale logo images on various real data.

This letter considers the distribution of product for two correlated real Gaussian random variables with nonzero means and arbitrary variances, which arises widely in radar and communication societies. We determine the exact probability density function (PDF) in terms of an infinite sum of modified Bessel functions of second kind, which includes some existent results, i.e., zero-means and/or independent variables, as special cases. Then, we study the approximation error and convergence rate when finite summations are exploited in practice. Finally, we evaluate the PDF behaviors of the derived expression as well as the Monte Carlo simulations.

In this letter, we propose a new algorithm to determine the position of the sidewalls by exploiting the multipath echoes of a target bounced from the sidewalls, which is useful to obtain the building layout, determine the relative position of the target in the room, and remove the higher order multipath ghosts for a through-wall tracking radar. Specifically, we first extract the 1-D trajectories of the real target and the multipath ghosts in each receive channel, based on the local maximum values extraction method and the 1-D Kalman filter. Second, by exploiting the principle of the first-order multipath echoes, the position of the sidewall is computed in each frame. In addition, the sidewall position can be obtained by averaging the coordinates of the target and the ghost in the presence of the second-order multipath echoes. Finally, the average of multiple frames is adopted to improve the detection accuracy. The proposed algorithm is validated by simulation and experimental data.

This letter considers the motion parameters estimation problem for a maneuvering target with arbitrary parameterized motion. The slant range of the target is modeled as a polynomial function in terms of its multiple motion parameters and a fast estimation method based on adjacent cross correlation function (ACCF) is proposed, where the iterative adjacent cross correlation operation is employed to remove the range migration and reduce the order of Doppler frequency migration. Then the motion parameters are estimated via Fourier transform. Compared with the generalized Radon Fourier transform (GRFT), the proposed method can estimate the parameters without searching procedure and acquire close estimation performance at high signal-to-noise ratio (SNR) with a much lower computational cost. Finally, simulations are provided to demonstrate the effectiveness.

This letter considers the coherent integration problem for a maneuvering target, involving range migration (RM) and Doppler frequency migration (DFM) within one coherent pulse interval. A new coherent integration method, known as Radon-Lv's distribution (RLVD), is proposed. It can not only eliminate the RM effect via jointly searching in the target's motion parameters space, but also remove the DFM and achieve the coherent integration via Lv's distribution (LVD). Finally, several simulations are provided to demonstrate the effectiveness. The results show that for detection ability, the proposed method is superior to the moving target detection (MTD), Radon-Fourier transform (RFT), and Radon-fractional Fourier transform (RFRFT) under low signal-to-noise-ratio (SNR) environment.

In this letter, we demonstrate an optimization problem of antenna placement of distributed multi-input multi- output (MIMO) radar. To evaluate the surveillance performance of the radar system, a coverage ratio is proposed as a criterion. Since the problem is of extremely huge computational complexity due to its complicated objective function and high dimensionality, we propose a solution that contains two parts: 1) a low- complexity method to simplify the objective function; 2) a placement algorithm based on particle swarm optimization (PSO) to deal with the challenge of high dimensionality. We also analyse the computational complexity of our solution. Simulation results verify the validity and advantage in computational complexity of our solution. Our contributions include a novel optimization placement model of distributed MIMO radar and a computational efficient solution to establish the optimal positions of antennas.

This letter considers the problem of building layout determination using through-wall-radar imaging technology, which employs multiple transmit-receive channels to implement multiview synthetic aperture imaging. In each view, the phase errors of multichannel data introduced by the unknown walls deteriorate significantly the performance of the coherently data-combined algorithm by Le Herein, we first obtain multiple single-channel building layout images for all independent channels of each view and propose a novel noncoherent fusion method named multiply-subtract-add to combine them into a single-view layout image. Then, we present an M- N- K detector plus median filtering to fuse multiple single-view layout images and reduce the existing cavities and burrs of wall images. The experimental results reveal that the presented noncoherent image fusion method gives the single-view layout images with higher signal-to-clutter-and-noise ratio than the conventional coherent algorithm based on data combination and a near-tidy panorama layout image is generated almost without the cavities and burrs.

We consider adaptive transmit and receive beampattern design for array radar systems. While adaptive processing is primarily employed for only receive beamforming in conventional design, we propose a fully adaptive approach involving jointly selecting the transmit correlation matrix and receive beamformer by maximizing the signal-to-interference-plus-noise ratio (SINR). The motivation of utilizing adaptive processing at the transmitter is that with imprecise knowledge of the interference (e.g., due to limited training data), only relying on adaptive receive beamforming may be inadequate for effective interference cancellation, whereas joint adaptive transmit and receive beamforming can afford a stronger ability to handle the interference. Simulations are provided to demonstrate the performance of the proposed joint beamforming approach.

This letter considers adaptive target detection problem using multiple-input multiple-output (MIMO) radar in the presence of spatially heterogeneous clutter. The covariances of the primary data and secondary data for the same and different transmit-receive pairs are modeled as different random matrices with partial priori knowledge of the environment. Two-step strategy is employed to design adaptive detector. Specifically, we first obtain the generalized likelihood ratio test (GLRT) detector by assuming the known matrices. Then, we derive the maximum posteriori (MAP) estimator of the covariance matrices by exploiting the priori information, and replace the given covariance matrices in the obtained GLRT with MAP estimates. Finally, we evaluate the proposed adaptive detector via numerical simulations.