Recursive techniques have demonstrated excellent computational efficiency in classic remote sensing tasks but are rarely applied in emerging remote sensing tasks in the IoT field, such as bioradar-based remote sensing of vital signs (VS). As a fundamental but important problem, VS sensing lacks an effective state model, making it difficult to implement using recursive techniques for a bioradar system. However, this paper presents an efficient recursive technique (BERT) that benefits remote VS sensing and accelerates computational speed with bioradar. Specifically, the recursive technique is derived from an efficient Markov state model based on the features of bioradar VS and is significantly helpful for algorithm implementation. This technique fills the recursive application gap in the VS sensing task and motivates further exploration of its rationale. Thanks to the strong capability of the recursive technique in computation, the proposed BERT requires less time and demonstrates greater accuracy compared to other methods in simulation results. We further conduct extensive experiments on two real datasets — one comprising 50 children and the other involving 30 adults across various scenarios, and the results show that the BERT algorithm significantly accelerates computation speed, reducing processing time by nearly 41% compared to the state-of-the-art algorithm, while maintaining superior estimation accuracy. Furthermore, this work also offers a fresh perspective on interpreting remote VS sensing for bioradar.

Radar, as a contactless, non-intrusive, weather-and light-independent sensor, works in complementary with other sensors. Radar-based Human Activity Recognition (HAR) has the advantages of privacy preservation and noise robustness. Therefore, it has a tremendous potential for IoT applications. Existing methods are dedicate to finding best radar representations for HAR. However, research on the elimination of non-activity information has not receive sufficient attention. In response to this question, the Crucial Region Search and Feature Discrimination (CRSFD) network has been proposed. It aims to automatically separate features and explicitly establish feature distribution, to accomplish non-activity feature elimination. The CRSFD consists of the Crucial Region Search (CRS) module and the Activity-Associated Feature Discrimination (AAFD) module. The CRS module, with edge protection and importance assessment capabilities, is more suitable for irregular changes in activity features. The AAFD module is customized to automatically and explicitly analyze the distributions of activity and non-activity features. Eventually, activity features are maintained for HAR. Experimental results on a human activity dataset and a human gesture dataset show that the proposed method has superior performance and is robust to noise.

The Wide-Angle Stare Synthetic Aperture Radar (WasSAR), as an emerging SAR observation mode, enables long-duration, multi-angular imaging of a scene, demonstrating remarkable advantages in three-dimensional (3D) information acquisition. A critical step in the process of 3D information extraction lies in accurately determining the displacement information between sub-aperture images captured from adjacent azimuth angles. During the WasSAR imaging process, spatial targets exhibit positional discrepancies in imaging results obtained from different azimuth perspectives, making pixel-wise displacement estimation in SAR images highly challenging, especially in complex scenarios. To address this challenge, this study proposes an innovative displacement estimation method for WasSAR imagery tailored for 3D information extraction. The proposed approach begins with brightness equalization preprocessing to harmonize the brightness distribution between two images, ensuring the accuracy of subsequent processing steps. This is followed by an initial estimation using block-based registration techniques based on the Enhanced Correlation Coefficient (ECC). Finally, an improved optical flow algorithm is employed to achieve precise displacement estimation, significantly enhancing the accuracy and reliability of displacement information estimation between SAR images. A series of experiments were conducted using Ku-band Mountain scene datasets acquired by the National University of Defense Technology. The experimental results include a comprehensive comparative analysis with several existing displacement estimation methods, showcasing the superior performance of the proposed algorithm across multiple key performance metrics. The proposed algorithm’s performance in 3D information extraction applications was also evaluated, confirming its effectiveness and high practicality.

Synthetic aperture radar (SAR) multiview observations can acquire omnidirectional scattering characteristics of targets, providing richer information for target detection and recognition. Deep learning has been widely applied to SAR image interpretation in recent years. However, the sensitivity of SAR images to imaging parameters and the limited training samples pose challenges in applying deep learning to SAR target detection. Therefore, this article first designs an SAR omnidirectional subaperture division data enhancement method with different aspect accumulation angles, multilook numbers, and subaperture overlap angles based on SAR imaging parameters and the original echo. Meanwhile, an omnidirectional SAR target detection dataset called SAR-Vehicle-Det is constructed using this subaperture division method to evaluate the model performance. Then, this article proposes a novel network attributed scattering center (ASC)-U2Det based on ASC guidance for SAR target detection. The prediction is divided into the ASC reconstruction image branch and the target detection branch. The reconstruction branch uses ASC reconstruction mask maps to guide the network to extract the SAR target scattering features at different aspect angles, suppress the background clutter interference, and improve the detection accuracy of the target detection branch. Finally, this article also evaluates the impact of imaging parameters with different subaperture accumulation angles and multilook numbers on the network detection performance. Experiments on the SAR-Vehicle-Det and miniSAR datasets show that the proposed ASC-U2Det outperforms many existing target detection algorithms.

In order to adapt to various detection environments on the ground, airborne synthetic aperture radar (SAR) as a remote sensing platform usually arcs along nonlinear trajectories, and the large accumulation angle in the circling process also improves the imaging effect. However, the arc motion demands rigorous control of the flying platform and precise measurement of the motion. In some cases, there is a significant discrepancy between the track recorded by the flight platform and the actual track, which not only affects the imaging effect but also interferes with the positioning and navigation of the platform. This article presents a new method of arc-line SAR positioning and imaging based on a closed-loop structure. The echo history extracted from a 1-D range profile is corrected using an echo-history correction factor (EHCF), which reduces the self-positioning error of the platform caused by the motion measurement device. This allows for accurate positioning of the flying platform and the acquisition of imaging results with superior focusing performance. The effectiveness and stability of the proposed method are proven by simulation and experimental results.

In this paper, an efficient method for parameter estimation of polynomial phase signal (PPS) is proposed. Instead of the existing methods based on parametric ergodic search or phase differentiation, the proposed method adaptively tracks the PPS phase through extended Kalman filtering (EKF), termed as APT-EKF. Firstly, based on the smoothness assumption of the local phase, a state-space model describing the PPS phase is constructed. Then, by solving the state-space model through EKF, the PPS phase can be tracked. Finally, the least square estimation (LSE) is performed for the inversion of PPS coefficients, and the O’Shea refinement strategy is implemented to enhance the estimation accuracy, thereby achieving the Cramér-Rao lower bound (CRLB). Compared with most existing studies, the proposed method occupies an obvious advantage in signal-to-noise ratio (SNR) threshold and computational efficiency. It is suitable for the arbitrary order PPS. Moreover, this article provides a comprehensive analysis of the parameter initialization, performance bounds, and computational complexity of the proposed method. Both simulation and experiment results are provided to demonstrate the effectiveness of the proposed method.

Circular synthetic aperture radar (CSAR) has garnered a lot of attention because of its exceptional capabilities. However, CSAR imaging is sensitive to terrain undulation errors due to the curved trajectory. The projection of an object whose height deviates from a reference height (RH) forms a circular ring in the full-aperture image. Presently, error compensation using a digital elevation model (DEM) is a prevalent solution to this issue. Although DEM can be inverted from echo data, the accuracy is unsatisfactory. Inspired by optical multi-focus image fusion methods, a CSAR multi-layer focusing imaging method is proposed in this letter. Firstly, CSAR sub-aperture images are used to construct multi-layer focusing images by displacement compensation, which is more efficient than applying an imaging algorithm directly. Secondly, the multi-focus image fusion method is applied to obtain a fully focusing image. Finally, the decision maps are used to invert the target region’s DEM. The multi-layer focusing imaging method can make objects of different heights focused. The fused result is fully focused and has a resolution of 1 m, depending on the CSAR system. The inverted DEM can visually describe the objects’ contours. Experimental results demonstrate the effectiveness and practicability of the proposed method.

Refocusing moving targets in synthetic aperture radar (SAR) poses inherent challenges. The difficulty is amplified when SAR raw data are missing in the azimuth direction, mainly because of the unknown motion parameters of non-cooperative targets. Estimating these parameters from SAR azimuth missing data (SAR-AMD) is notably more challenging than from complete echoes. To address this problem, we propose the MPE-CM method, a fast and robust motion parameter estimation method for SAR-AMD based on contrast maximization. Initially, following the range walk correction (RWC) by Keystone transform (KT), a coarse-focused image is derived in the range-Doppler (RD) domain by constructing a phase compensation function with varying focusing factors. Subsequently, the estimation of motion parameters is converted into the estimation of focusing factors, which is accomplished through the maximization of contrast in the coarse-focused image. Concurrently, we propose a Five-Point method to efficiently and robustly determine the focusing factor. Finally, the along-range and azimuth velocities can be retrieved from the estimated focusing factor and Doppler shift, where the Doppler shift is obtained by identifying the peak energy shift of the coarse-focused image. The proposed MPE-CM method, ensures computational efficiency through a fast and robust approach, while coherent processing improves its anti-noise performance. The experimental results demonstrate the effectiveness of the proposed MPE-CM method in SAR-AMD.

A unique mode known as wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) allows for the dynamic surveillance of moving targets over a wide range of azimuth angles. Despite WasSAR-GMTI develops rapidly, the parameter estimation and trajectory reconstruction of moving target in a three-Dimensional (3-D) field have not been solved well in WasSAR. In order to address this problem, a framework based on 3-D velocities’ and 3-D positions’ estimation is proposed in this article. On account of the derived equivalence, it is possible to characterize moving targets in WasSAR-GMTI and achieve the decoupling of velocity and position. First, for the multidimensional velocities’ estimation, the joint interferometric phases of several subapertures are utilized. Then, to estimate the multidimensional positions, the signal of moving target in WasSAR is modeled as a polynomial-phase signal (PPS). We tackle the issue of 3-D positions’ estimation through the coefficients of PPS estimated by cubic phase function (CPF) method. Finally, 3-D trajectory reconstruction of moving target is accomplished by combining the results of multiaperture estimation. The proposed method extends the parameter estimation to the 3-D case, and the applications of WasSAR-GMTI are spread. Moreover, the parameter estimation of moving target is addressed independently, namely without auxiliary information of priori road detail. The estimation accuracy of the proposed method is evaluated by the simulated data, and the validity and feasibility of the proposed method are demonstrated through the results of real data.

For airborne repeat-pass synthetic aperture radar interferometry (InSAR), residual motion errors (RMEs) exist between the true and measured trajectories due to the inaccuracy of current navigation systems. In addition, a global unknown phase ambiguity (PA) exists in the unwrapped phase due to the error of absolute phase estimation. The RME and PA are significant error sources in the repeat-pass InSAR, which can cause phase errors in the final interferograms. In general, an external digital elevation model (DEM) can be used to estimate the RME and PA. However, it is hard to find a high-precision external DEM matching with the airborne InSAR data. Compared with the traditional InSAR technique, the 360° aperture gives circular InSAR (In-CSAR) the capability to estimate the RME and PA without external DEMs. Unlike previous approaches, in this article, a multiangle observation model is established to estimate RME and PA simultaneously. This model registers the DEM images obtained from different observation angles. In the field of image registration, the Demons algorithm is often used for nonrigid registration. We use, for the first time, the Demons algorithm to register multiangle false DEM images containing height errors and obtain the reference DEMs. Due to the gradient of the DEM image being destroyed by the RME and PA, a two-step preprocessing is proposed to achieve subpixel registration of multiangle false DEM images. After obtaining the reference DEM, the least square (LS) or robust mixed integer linear programming (RMILP) can estimate the unknown RME and PA. The results show that, with respect to the true values, the root-mean-square error (RMSE) of the calibrated DEMs is 0.37 m for the simulated dataset and 1.17 m for the real dataset. After correcting the RME-induced and PA-induced height errors, the RMSE of the DEM is improved by at least 92.6% for the simulated dataset and by at least 70.8% for the real dataset.

The acquisition of 3-D information in the observation scene has always been a prominent issue in the field of synthetic aperture radar (SAR). The emerging wide-angle staring SAR (WasSAR) utilizes its unique multiview observation performance to obtain offset information of the target position under different observation angles, enabling the acquisition of 3-D information of the observation scene. However, existing multiview 3-D information extraction methods suffer from large errors due to a lack of prior information about the observed scene. In order to address these challenges, this article analyzes the impact of the missing incidence angle information on the 3-D reconstruction results and proposes a 3-D information correction algorithm. The method only needs the single-channel echo information of the two azimuth angles of a single pass and the corresponding radar platform motion information and does not need to rely on any a priori information of the observation scene, which realizes the acquisition of 3-D information without a priori information in the real sense. Through simulation experiments, we quantitatively analyze the error transfer coefficient and confirm both accuracy and effectiveness through experimental data processing in Ku-band mountain scenes autonomously measured by our team. The proposed algorithm significantly enhances measurement precision and reliability, demonstrating a mean error reduction to just 49% and a root-mean-square error (RMSE) decrease to 46% compared to the traditional method, thereby confirming its superior performance and practicality.

In view of the complex imaging environment faced by penetrating radar, low-frequency ultra-wideband (UWB) signals are typically employed to achieve better penetration performance and finer distance resolution. Under the conditions of UWB near field, the positioning error of the radar moving platform can have an unignorable impact on the imaging outcome; in a through-the-wall environment, the refraction effect compensation errors brought about by the environmental parameters of wall also severely affect the image quality. At present, existing research can only address one of the platform positioning errors and wall refraction compensation errors, assuming the other to be in an ideal state. However, in practical application, these two types of errors usually coexist, therefore, in this paper, we propose a synchronous compensation method that can jointly estimate radar positioning errors and environmental parameters for compensation, breaking the coupling between the two types of errors and achieving clear imaging of unknown building layouts and internal targets. This method is suitable for Synthetic Aperture Radar (SAR) moving along walls and around buildings. Simulation and measurement experiments have proven the above content.

The low-band synthetic aperture radar (SAR) is a system that can present foliage-penetrating (FP) targets and exposed strong-scattering targets while the high-band SAR system can present exposed targets and texture information except for FP targets. Therefore, the different features of co-registered low-band and high-band SAR images can theoretically reveal FP targets with some disturbances. However, the co-registered dual-band SAR images cannot do the difference operator directly for the distinct statistical property of the SAR image pair. The traditional method is to transfer the background texture from the high-band SAR image to the low-band SAR image by the cycle-consistent generative adversarial network (CGAN) method. However, CGAN would generate discontinuity and mistakes in transferring the large-scale texture information, and the complex network would heavily increase the time complexity of the application. To overcome this issue, we first innovatively introduce a fast and accurate texture-transferring method based on the dual-band SAR image fusion (DBIF), then we further combine the DBIF-based texture-transferring method with the difference operator and the threshold segmentation, and finally we get a novel DBIF-based FP target detection (DBIFFPTD) method. To verify the feasibility of the DBIF-based texture transfer method, we discuss the reflection form of different landscape objects at dual-band waves and estimate the theoretical texture transferring the image. Experiments on the open dual-band AIR-MD-SAR dataset and the independently measured dual-band SAR dataset show that the novel DBIF performs better than CGAN in transferring texture information of dual-band SAR images. Besides, dual-band linear SAR (LSAR) and circular SAR (CSAR) FP experiments are both conducted to show that the proposed DBIFFPTD has wide applicability in the flight track and is superior to the traditional low-band double-parameter constant false alarm rate (DCFAR) detector-based FPTD (DCFARFPTD) method and the dual-band CGAN-based FPTD (CGANFPTD) method.

Ground moving targets with complex motions often appear seriously dislocated and smeared in synthetic aperture radar (SAR) imagery due to non-cooperative motion. Refocusing such targets in SAR is challenging because of unknown motion parameters. In this paper, a novel SAR moving target imaging method based on adaptive phase tracking (MTIm-APT) is proposed. First, the Hough transform (HT) and the second(2nd)-order Keystone transform (SOKT) are performed to correct the range migration. Second, the Doppler phase can be adaptively tracked based on the improved extended Kalman filter (EKF). With the tracked Doppler phase, the motion parameters required for moving target imaging is estimated. Finally, the moving target is well-focused after motion parameters compensation since the high-order Doppler phase errors are efficiently eliminated. Unlike existing research that only considers the 2nd- or third(3rd)-order Doppler phase, the proposed method considers higher-order Doppler parameters which can be simultaneously estimated, thus eliminating error propagation effect. More importantly, due to the suboptimal filtering characteristics of EKF, the proposed method maintains excellent imaging performance at lower signal-to-noise ratio (SNR) than classical PGA. On the other hand, our method has a certain advantage in computational efficiency. Both simulated and real data processing results are provided to validate the feasibility and effectiveness of the proposed SAR MTIm-APT method.

It is of great importance to remotely sense trapped victims with radio signals in modern search and rescue after natural disasters like earthquakes, avalanches, building collapses, and so on. Various radio sensors have been developed to date; however, they are hardly deployed to recognize efficiently the vital sign in long-distance, deep-coverage, and multi-subject situations because the back-scattered victim-critical radio signals are really weak and are nearly drowned in the ambient nonstationary noise and clutter. To tackle the formidable difficulty, we present a four-dimensional wideband microwave radar operating at 1.7 GHz to 2.7 GHz and develop a spatio-temporal processing algorithm to fully explore the vital knowledge of victims in the three-dimensional spatial and one-dimensional temporal information. We conducted comprehensive field experiments in real post-disaster environments and demonstrated experimentally that our radio sensor can continuously monitor multiple survivors trapped under mounds of debris in real urban environments. Moreover, we demonstrate that the presented method can achieve a signal-to-noise-and-clutter ratio improvement of more than 20 dB even in the case of deep burial, which enables localizing the victims trapped in the order of ten meters and recognizing the survivors’ vital states. We expect that the presented strategy may open an avenue for future remote life-rescuing and beyond in practical applications.

The fundamental difficulties in the supervised deep learning algorithm are obtaining large-scale labeled data and generalizing the trained model to a new environment. In this letter, we propose an unsupervised domain adaption method for human activity classification using micro-Doppler signatures. We study on how to classify micro-Doppler signatures in a new domain using only labeled samples from a different domain, mainly focus on simulation-to-real-world deep domain adaptation. First, we use motion capture (MOCAP) database to generate simulated micro-Doppler data to train the convolutional neural network (CNN). Then, considering the difference between simulation and real-world domain distributions, we introduce a domain discriminator to pit against the feature extractor part of the CNN. Through this adversarial process, like the generative adversarial network, the CNN trained on the simulation domain is able to generalize to the real-world domain. Experiment results show that the proposed method achieves over 84.02% accuracy in real-world micro-Doppler classification, which outperforms nearly 16% in CNN trained on the annotated simulation without domain adaptation and performs better than the existing domain adaptation methods.

Deep neural networks have shown promise in the radar-based human activity analysis application. Different from existing deep learning models that take either micro-Doppler spectrograms or range profiles as their input, the proposed method can process micromotion signatures in a 3-D way. In this letter, we first transform radar echoes into range-Doppler (RD) time points and then directly process the point sets via a designed 3-D network called the RD PointNet. In fact, our point model is a discrete representation of the motion trajectory. Through this quantitative model, we can use the 3-D network to simultaneously capture human motion profiles and temporal variations. The motion capture simulations and ultrawideband radar measurements show that the proposed framework can achieve superior classification accuracy and noise robustness when compared with image-based methods.

This letter is devoted to the layout reconstruction via the ultra-wideband (UWB) through-wall imaging radar under one single observation and simultaneously takes account of the real-time human indication. In the proposed framework, layout reconstruction is taken as the preprocessing, where a coherent processing interval consisting of several successive received echoes in the initial stage is first employed to construct a range-Doppler (RD) spectrum. Then, in the RD spectrum, a series of selected discrete Doppler frequency signals is used to form Doppler back projection (BP) images. Finally, in the Doppler BP image stack, we design a 3-D constant false alarm rate detector to extract the building layout. Once completed, the achieved layout as auxiliary information is fused with the simultaneous human indication. Through-wall experiments show that the proposed method can effectively extract the covered layout of multiple walls under one single view and accordingly provide strong support for the concealed human sensing.

The work presented in this letter focuses on the time-variant radio frequency interference (RFI) issue in the low-frequency ultra-wideband (UWB) radar. Different from many previous studies, we first analyze the characteristics of RFIs and scattered echoes in the slow-time dimension and take advantage of overlapped short-time Fourier transform to adapt to the time-variant RFIs and update the frequency Doppler spectrum. Then, in the frequency Doppler spectrum, we adopt the minimum statistic combined with 1-D cell-averaging constant false alarm rate to estimate and separate the RFI power spectrum from the scattered echoes based on their differences. Finally, to mitigate the estimated RFIs, a suboptimal filter controlled by the defined entropy of range profiles after math filtering is designed. Employing a UWB radar, different experiments were conducted, and results verify the proposed method.

In through-wall indication of a moving human target in enclosed structures, a shadow effect because of the human target blocking parts from illumination on the back wall will emerge, referred to as a “ghost” in indication results. The shadow ghost moves as the human target does, which makes causal change detection (CD) invalid to separate them. To mitigate the shadow ghost, we analyze its differences from the moving human target. Based on the difference that the illumination is only blocked in partial channels of the multiple-input–multiple-output (MIMO) array while target echoes exist in most channels and the fact that shadow ghosts overlap more between successive indication results than the imaged targets as a result of their larger size, we proposed a mitigation method including a coherence factor and noncausal CD processing. Through-wall experiments via a MIMO through-wall imaging radar validate the proposed method.