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.

Active sensing, which requires signal transmission, offers high detection performance but suffers from poor stealth capability. In contrast, passive sensing offers strong stealth capability by exploiting non-cooperative illuminators of opportunity (IOs), but its detection performance is weaker due to the unknown IO signals. This paper proposes a target detector for multistatic hybrid active-passive sensing (HAPS) systems based on the generalized likelihood ratio test (GLRT) criterion. The proposed detector aims to combine active and passive sensing strengths to mitigate their respective limitations and enhance overall low-interception detection performance. A test statistic is formulated by integrating both active and passive observations, which is then decomposed into several likelihood functions to reduce computational complexity. Nuisance parameters are estimated within each function and replaced with their maximum likelihood estimates. A low-dimensional GLRT test statistic for HAPS is established by fusing these reduced-dimensional likelihood functions. Simulations show that the proposed detector outperforms purely active or passive detectors, highlighting its superior performance and robustness.

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.

This paper investigates subspace-based target detection in OFDM integrated sensing and communications (ISAC) systems, considering the impact of various constellations. To meet diverse communication demands, different constellation schemes with varying modulation orders (e.g., PSK, QAM) can be employed, which in turn leads to variations in peak sidelobe levels (PSLs) within the radar functionality. These PSL fluctuations pose a significant challenge in the context of multi-target detection, particularly in scenarios where strong sidelobe masking effects manifest. To tackle this challenge, we have devised a subspace-based approach for a step-by-step target detection process, systematically eliminating interference stemming from detected targets. Simulation results corroborate the effectiveness of the proposed method in achieving consistently high target detection performance under a wide range of constellation options in OFDM ISAC systems.

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.

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 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.

Range gate pull-off (RGPO) jamming is an electronic countermeasure widely used to fool radar tracking systems. Nevertheless, the research on its strategy optimization has only been tepid. The optimization model, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. The algebraic description for the objective function of the optimization of the RGPO jamming strategy is difficult to obtain, and the jamming results are not deterministic under the influence of the input noise. To address these issues, this article models the generation of the RGPO jamming strategy as a stochastic simulation optimization (SSO) problem, and proposes an optimization algorithm of the RGPO jamming strategy with the help of SSO technologies. We propose the committee-based active learning (CAL) assisted optimal computing budget allocation-based particle swarm optimization (CALPSO-OCBA) to alleviate the competition between solution space search and candidate solution performance evaluation by embedding CAL into PSO-OCBA. To improve the feasibility of the proposed algorithm, a scoring scheme that does not rely on the internal knowledge of the radar tracking system (i.e., the tracking model, tracking method, and tracking parameters) is designed. In addition, we use four most widely used tracking problems as the benchmarks for testing the proposed optimization algorithm of the RGPO jamming strategy. Experimental results demonstrate that the proposed algorithm is highly competitive in solving the optimization problem of the RGPO jamming strategy.

In this article, we develop a multiframe detection architecture relying on the generalized likelihood ratio test (GLRT) to address complicated multitarget detection and tracking in radar systems. The commonly used and unrealistic assumptions made in the previous works, where targets are sufficiently far apart from each other and do not occupy the same measurement cell (only for high-resolution radars), can be relaxed. Specifically, at the design stage, we take full account of the energy integration of real targets over multiframe measurements and the accurate data association of multitarget tracks for arbitrarily located targets. To solve the above challenges, a new detection statistic including the amplitude likelihood and the transition cost of a search path is derived. Then, we come up with two detection schemes for both known and unknown number of targets consisting of the log-likelihood ratio, an offsetting term about the transition cost and a penalty term accounting for the unknown number of targets. Finally, a fast implementation for the above detectors is investigated to trade detection and tracking performance for a lower computational complexity. Interestingly, the proposed architectures can not only improve detection performance for multiple dim targets through noncoherent integration, but also suppress tracks swap or mispairing among multiple frames when the targets are close. Numerical results and tests with real radar data for various multitarget scenarios are provided to demonstrate the effectiveness of the proposed algorithms.

Proper prior knowledge of target scattering centers (SCs) can help to obtain better detection performance of range-spread targets. However, target SCs are sensitive to the target’s attitude relative to the radar and vary significantly among different targets. The existing approaches that employ predetermined prior knowledge may suffer performance degradation when the prior information does not match the practical scenarios. A possible way to circumvent this drawback is to estimate the SCs of different targets adaptively and check the presence of a target utilizing the range cells occupied by the most likely target SCs. For this reason, this article develops a generalized likelihood ratio test based on adaptive SCs estimation (ASCE-GLRT) for range-spread target detection in compound-Gaussian clutter. Under the assumption that the target SCs are sparse, we model the problem of SCs estimation as a sparse signal representation. Moreover, since the sparse assumption may not always be satisfied in practice, a modified sparsity regularization method is proposed to enhance the robustness of the estimation performance of targets with different scattering characteristics. A theoretical analysis shows that the proposed detector can achieve the constant false alarm rate (CFAR) property. The performance assessments conducted by numerical simulation and field tests confirm the effectiveness and robustness of the proposed detector.

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.

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 paper, a direct target localization algorithm with quantized measurements for non-coherent MIMO systems is proposed. In this system, each receiver transmits a low-bit quantized version of the echo rather than the full raw data to the fusion center. We construct a joint likelihood function based on the low-bit data of each receiver, from which the unknown target position can be directly determined. The Cramer-Rao lower bound (CRLB) is derived to analyze the localization performance of our proposed algorithm. To maximize the localization performance, a CRLB-based objective function is designed to obtain the optimum quantization thresholds. The formulated problem is a high-dimensional and non-convex optimization problem that when solved, determines two types of coupled parameters for the quantization thresholds and the complex-valued scaling coefficients of the signal. We propose a batch gradient descent embedded particle swarm optimization algorithm to solve this problem effectively. Numerical results show that the proposed algorithm delivers superior performance in terms of maximizing the overall localization performance, and the 3-bit quantized algorithm is able to provide performance that is very close to the unquantized algorithm. Experimental data recorded by three small radars are also provided to demonstrate the effectiveness of the proposed algorithm.

Wireless sensor networks (WSNs) have been demonstrated to enhance parameter estimation performance for target tracking. In this paper, a prior information based quantizer design framework is proposed for target tracking for WSNs. In the proposed framework, the imperfect wireless channels between local sensors and the fusion center are considered. To make full use of the historical states and measurements embedded into the Bayesian tracking methodology, the quantizer design is suggested to be implemented with considering the prior state information. To this end, a channel-aware posterior Cramér-Rao lower bound (PCRLB) is derived based on the state prediction and further used as the performance indicator for quantizer design. Regarding target tracking, we model the quantizer design problem as a non-convex and highly nonlinear optimization problem that is intractable in general. We split the problem in terms of different scenarios, and for one-bit quantizer design based on a binary symmetric channel (BSC), we find that the optimal solution can be analytically computed. While for the general fading channel-based quantizer design problems, we propose two polynomial-time algorithms to find the solutions. Meanwhile, an approximation-based channel-aware particle filter (A-CAPF) is proposed to improve the implementation efficiency of state filtering. Simulation results demonstrate the enhanced performance and execution efficiency of the proposed algorithms in the context of the BSC and Rayleigh fading channel.

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.

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.

The multi-frame detection (MFD) with range-Doppler-azimuth measurements has been shown to be significant in detecting and tracking weak targets in active radar and sonar systems. Existing methods suffer from integrated energy loss for a long time noncoherent observation sequences due to approximate target evolution model or rough energy integration strategies. Moreover, it is hard to maintain consistency of multi-frame integration for targets in different near-field and far-field regions. By carefully mapping echo measurements to a discrete grid space without the introduction of new conversion errors, we proposed an efficient solution to build an accurate grid state model based on sensor parameters. Then, the combination of the grid state model and target evolution relationship among adjacent frames enables an adaptive MFD implementation. Each search path during multi-frame integration is adjusted adaptively with different grid state sizes. Finally, an improved strategy based on the predicted estimate is further presented to reduce the complexity of algorithms and improve the accuracy of possible search paths. The proposed methods can effectively integrate target energies among multi-frame range-Doppler-azimuth measurements, and maintain the robustness of the long time integration for targets in different near-field and far-field regions. Numerical results and tests with real radar data further show that the proposed method achieves high detection probability and tracking accuracy for weak targets with range-Doppler-azimuth measurements.

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.

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.

A key issue in multi-sensor surveillance is the capability to surveil a much larger region than the field-of-view (FoV) of any individual sensor by exploiting cooperation among sensor nodes. Whenever a centralized or distributed information fusion approach is undertaken, this goal cannot be achieved unless a suitable fusion approach is devised. This paper proposes a novel approach for dealing with different FoVs within the context of Generalized Covariance Intersection (GCI) fusion. The approach can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic tracking scenarios demonstrate the effectiveness of the proposed solution.

In this paper, we address the problem of multisensor management for multi-target tracking via labeled random finite sets (LRFS) in sensor network systems which require both precision and real-time. Considering the optimal multisensor management strategy (proposed in [1] named joint decision making (JDM) algorithm) suffers from the high-dimensional computational complexity, to compromise between tractability and fidelity, an alternative multi-sensor management strategy is proposed. By sequentially calculating the Cauchy-Schwarz (CS) divergence between global generalized Covariance Intersection (GCI) fusion result and the GCI fusion result of two sensors, the JDM algorithm is simplified as a hybrid decision making with a two-dimensional optimization problem, which is referred to as the HDM algorithm, and meanwhile the proposed HDM algorithm is superior to the independent decision making (IDM) algorithm [1] in precision due to the IDM algorithm completely ignores the correlation among sensors. The computational complexity of the proposed method is also provided by comparison with the JDM and IDM algorithms. The efficiency as well as the performance of the proposed method is well demonstrated in a challenging multisensor multi-target tracking scenario by numerical results.

This paper proposes a computationally efficient distributed fusion algorithm with multi-Bernoulli (MB) random finite sets (RFSs) based on generalized Covariance Intersection (GCI). The GCI fusion with MB filter (GCI-MB) involves the computation of the generalized MB (GMB) fused density determined by a set of hypotheses growing exponentially with object number. Hence, its applications with multiple targets are quite restrictive, which further motivates an efficient fusion algorithm. In this paper, we propose a novel approximation of the GCI-MB fusion. By discarding the hypotheses with negligible weights, the GCI-GMB fusion amounts to parallelized fusions performed with several smaller groups of Bernoulli components. As such, the computation of the GMB fused density is significantly simplified, with the number of hypotheses reduced dramatically and a practical appealing parallelizable structure achieved. Based on the proposed approximation, a computationally efficient GCI-MB fusion algorithm which can harness large amount of objects is devised. Furthermore, we present the analysis on both the characterization of the L1-error and the computational complexity of the proposed fusion algorithm compared with the standard GCI-GMB fusion. Our analysis shows that the proposed fusion algorithm can reduce the computational expense as well as memories dramatically with slight approximation error. Numerical experiments using the Gaussian implementation for a challenging scenario with twenty objects demonstrate the performance of the proposed fusion algorithm.

In this paper, the out of sequence measurement (OOSM) problem with arbitrary lags in nonlinear dynamic systems is considered. We develop an efficient particle filtering (E- PF) algorithm based on the exact Bayesian solution. Generally, by introducing some reasonable Gaussian assumptions, a general Gaussian smoother is derived to compute the expected smoothing pdfs instead of using the particle smoother, which makes the E-PF computation efficient and applicable for most nonlinear cases. Meantime, for E-PF, only the estimates and covariances for a predetermined maximum number of lags are stored, the storage resource is also effectively saved. In the simulation, a two-dimensional target tracking example is given, the numerical results show that the tracking performance of our algorithm is quite close to the A-PF algorithm proposed by Zhang et al., while the computation cost is significantly reduced.

Multi-frame detection (MFD) and multi-sensor fusion are two popular methods of target detection and estimation which can improve the performance by increasing the number of measurement samples. In this paper, we combine these two methods together, proposing a novel multi-sensor multi-frame detection (MS-MFD) method. On the one hand, MS-MFD can make use of the target information as much as possible through the multi-frame integration. On the other hand, it can acquire the target space-diversity gain by jointly processing the measurement samples on different observation orientations, providing more accurate estimates. In particular, the proposed method consists of two steps. First, it conducts the MFD processing in each sensor node, computing the local multi-frame jointly posterior probability density. Then, it transmits the local densities to the fusion center for further processing, calculating the global target estimates. Furthermore, in order to improve the implementation efficiency of MS-MFD, a Gaussian Mixture model based method is proposed to approximate the distribution of local posterior probability density, so that the transmission costs of local posterior probability density can be significantly reduced. It is demonstrated by simulations that the proposed methods show superior performance.

In this paper, a new power allocation framework is proposed with the task of multiple target tracking (MTT), in which an adaptive cost function (ACF) with respect to the transmit power and tracking accuracy requirements is first designed. Then we take the ACF as an objective function and formulate the proposed framework as a mathematical optimization problem. In this problem, the posterior Cramér-Rao lower bound (PCRLB) provides us with a lower bound on the estimated error of the targets state. Numerical simulation demonstrates that in the scenario where the common method is not applicable, an effective and robust power allocation scheme can be obtained by the proposed method.

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.

This paper considers the detection problem and its realistic implementation for multiple-input multiple-output (MIMO) radar with widely separated antennas. In particular, since the range cells of different transmit-receive channels are not in superposition, but intersecting with each other, it is difficult to determine, by gathering measurements from all transmit-receive channels, whether a target is present in an interested resolution cell. Besides, because of the intersecting of transmit-receive channels, the direct thresholding processing, even with the ideal detector, will result in enormous false alarms, which we refer to as “ghost targets” in this paper. To address these realistic detection problems, we propose a two-steps detection procedure based on grid space searching. Specifically, the measurements are organized according to a carefully designed grid space. Then a two-steps detection procedure is performed. The first step is to design a detector and determine the target existence for each channel in a grid cell. The second step is devised to eliminate the ghost targets to decrease the false alarms. Eventually, extensive simulations are provided to validate the efficacy and feasibility of the proposed procedure.

This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these issues, in this paper, a likelihood-based asynchronous batch estimation (ABE) scheme is developed, wherein local likelihood function is regarded as the local information to ensure a high fusion accuracy, and the asynchronous likelihood functions of the multiple sensors during a predefined update period are fused to jointly estimate the target states. Then, to implement this framework distributively using particle filter, a likelihood-based ABE DPF (LB-ABE-DPF) algorithm is proposed. In addition, to achieve low communication requirements, the likelihood function is parametrically represented by polynomial approximation and least square (LS) approximation strategies. Numerical results show the efficiency of the proposed algorithm.

This paper considers the multi-target tracking (MTT) problem in multi-input multi-output (MIMO) radar systems with the “defocused transmit-focused receive” (DTFR) operating mode, in which each transmitter forms a defocused beam to illuminate the whole surveillance region and each receiver adopts a focused beam to acquire a high angular resolution. When MIMO radars work in the DTFR operating mode, asynchronous data fusion (ADF) becomes very challenging since the measurements from the same target are acquired by different receivers at different times. To address this problem, we develop a novel batch estimation approach. By incorporating a time-aligned strategy, the local measurement as well as the most recently received asynchronous measurements from other receivers during a predefined update period are fused to jointly estimate the target state. In addition, motivated by the benefits of decentralized fusion architecture, a decentralized batch estimation (DBE) approach and its particle filtering based implementation (DBE-PF) are both presented. Finally, a target tracking scenario is also provided to demonstrate the effectiveness of the proposed DBE-PF approach.

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.

In this paper, we consider an adaptive node and power simultaneous scheduling (ANPSS) strategy for target tracking in distributed multiple radar systems. For all of the available nodes, with full resources allocation, minimizing estimation mean-square error (MSE) may exceed the predetermined system tracking performance goal and cause unnecessary resources consumption. Therefore, tracking performance driven resource allocation schemes for multiple radar systems are proposed. For a predefined estimation MSE threshold, the total transmitted energy is minimized by optimally scheduling node and power resources with the required tracking accuracy. For a given total power budget, the attainable tracking MSE is minimized by optimizing node and power allocation among the transmit radars. The Bayesian Cramer-Rao lower bound (BCRLB) is used as a performance metric. The resulting optimization problems are solved through Zoutendijk method of feasible directions (ZMFD). Numerical results demonstrate that significant resource savings could be obtained through the proposed schemes.

In this paper, we propose an adaptive node selection strategy for target tracking in passive multiple radar systems, with the objective of minimizing the number of nodes in the tracking task. Since the signal parameters are random in passive systems, we first take the expectation over the random parameters, and derive a new Bayesian Cramer-Rao lower bound (BCRLB) as the criterion. Then, we formulate a knapsack-based node selection problem with the required tracking accuracy constraint. This formulation can be solved optimally by an exhaustive search algorithm, but with high computational complexity. For real-time application, we propose an efficient heuristic algorithm to solve it, which offers considerable reduction in computational complexity. Numerical results demonstrate the superior performance of the proposed strategy and the effectiveness of the proposed solution.

In this paper, we address the target detection problem using multi-sensor dynamic programming based track before detect (DP-TBD) methods. First, we give two implementation methods of multi-sensor DP-TBD under the centralized processing and the distributed processing, respectively. Then, in order to improve the implementation efficiency of the multi-sensor DP-TBD, we further propose an improved DP-TBD method based on the approximation of local likelihood. Particularly, the proposed method first calculates the likelihood locally in the sensor nodes, then approximates the likelihood with a weighted sum of a number of basis functions, and finally transmits the weighted coefficients rather than all likelihood to the fusion center for further processing with DP-TBD. By this means, the proposed method can reduce the communication requirements of the system. In addition, since the likelihood are calculated locally, the computational burden of the fusion center can also be alleviated. The analysis and simulation results demonstrate that the proposed method can improve the implementation efficiency significantly with limited performance loss in comparison with the centralized/distributed processing DP-TBD.

In this paper, we consider the fluctuating targets detection problem in a distributed multi-sensor network. A multi-sensor multi-frame track-before-detect (MS-MF-TBD) procedure is proposed to sufficiently make use of the target energy diversity in space and time dimensions (space-time diversity). Two MS-MF-TBD methods, the multi-sensor maximum likelihood-probabilistic data association (MS-ML-PDA) and the multi-sensor dynamic programming based TBD (MS-DP-TBD), are derived, and a number of simulation experiments under different target fluctuation models are performed. Through these simulations, we demonstrate that by using the space-time diversity, MS-MF-TBD methods can efficiently detect the fluctuating targets, achieving significant detection performance gains in comparison to either the single sensor TBD methods or the conventional multi-sensor detection methods.

In this paper, we will investigate a joint beam and dwell time allocation strategy for multiple targets tracking based on the phased array radar system. We achieve the resources allocation by formulating and solving an optimization problem, which is to minimize the total dwell time on all targets with the tracking accuracy of each target satisfying a pre-designed requirement. Since the Bayesian Cramer-Rao lower bound (BCRLB) provides a lower bound on the error of any unbiased estimator, it is employed as the metric for the tracking performance. The optimization problem is proved nonconvex and we solve it through a two-step decomposition algorithm. Simulation results show that the optimization strategy we propose is effective in resource saving and favourable for achieving a better tracking performance of worse targets compared to the operating mode with uniform resources allocation.

In this paper, a joint revisit and dwell time management (JRDTM) strategy for single target tracking based on the predicted Bayesian Cramer-Rao lower bound (BCRLB) in phased array radar system is addressed. We achieve the time resources management by formulating and solving an optimization problem, which is to minimize the resource amount used for tracking with the tracking accuracy of the target meeting a predesigned threshold. As the BCRLB provides a lower bound on the estimated mean square error (MSE) of target state, the predicted BCRLB model with time resources is derived and employed as the metric for the tracking performance. We put forward a converted algorithm to settle the optimization problem subsequently. Simulation results demonstrate that the proposed joint management strategy can help to achieve the tracking performance with less resource consumed.

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 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.

A sparse and uniform multiple-input-multiple-output array is generally utilized in through-wall radar to implement real-time imaging of moving targets. However, the array sparsity with the interelement spacing much bigger than half a wavelength gives rise to grating lobe interference smearing the images. In order to enhance the signal-to-interference ratio, this letter introduces the sign coherence factor (SCF) to weigh through-wall images to suppress the grating lobes. The SCF is first proposed in medical ultrasound imaging and directly reflects the coherence of sign bits of all the transmit-receive channels in each pixel. Since the SCF calculated only by the sign bits, it has the smallest amount of computations compared with two other weighing factors, namely, the coherence factor (CF) and the phase coherence factor (PCF), which have been applied to through-wall imaging already. Moreover, the SCF has comparable performance in suppressing grating lobes with the PCF, much better than the CF. These two advantages make the SCF the most suitable for real-time imaging of moving targets. The experimental results with a two-transmitting eight-receiving stepped-frequency continuous-wave through-wall radar verifies the excellent performance of the SCF.

Wall penetration of the electromagnetic wave causes target image defocused and displaced from its true position in through-wall radar imaging. To solve this problem, this letter proposes an approximate wall compensation algorithm, named the refraction angle approximation algorithm, which assumes that the actual refraction angle is approximately equal to the one whose incidence angle is the azimuth angle of the target. The assumption is reasonable since the refraction angle is less sensitive to the incidence angle and limited in a small range based on the Snell's law, and the cosine function has a small derivative at a small angle. Theoretical derivation indicates that the time delay estimation error can be ignored if the target is not so close to the wall surface around the radar. Numerical simulation verifies the efficacy of the algorithm.

In this letter, we derive the distribution characteristics of first-order multipath ghosts in a nested multiple-input-multiple-output (MIMO) through-wall radar and evaluate the efficacy of the phase coherence factor (PCF) in ghost suppression. Different from a synthetic aperture radar, the first-order multipath echoes of a nested MIMO through-wall radar generate several ghosts. For example, for a nested MIMO array composed of a compact receiving subarray and M spatially dispersed transmitters, there are M ghosts at the same side of the wall as the array. The mth ghost is supposed to occur near the intersection of the line, connecting the target and the center of the receiving subarray, and the ellipse whose foci are the positions of the target and the mth transmitter. Under the assumption of phase uniform distribution clutter, the PCF can suppress the ghosts up to -20 lg(1 - √(M2- 1)/M2) dB, which is about 17.46 dB when M = 2.

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.

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.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure. An incorrect Doppler centroid would cause a loss of SNR, an increase in the azimuth ambiguity level, a shift in the location of the target, etc. An improved Radon-transform-based Doppler centroid estimation scheme of bistatic forward-looking SAR is proposed in this letter. First, this scheme performs edge detection on the range-compressed data and then does the coarse and fine Radon transforms to estimate the Doppler centroid. Simulations and real-data experiments validate the effectiveness of this method.