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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.

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.

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.

This paper aims to the joint design of transmit waveform and receive filter to enhance the clutter suppression capability for airborne radar with non-uniform pulse repetition interval (NUPRI). Specifically, a multipulse echo model with NUPRI incorporating a moving point-like target and signaldependent clutter is first established. Then the two-dimensional range-Doppler plane is acquired by performing pulse compression and the windowed pulse Doppler processing on the echo. Further, the signal to interference plus noise ratio (SINR) of the interested area in the range-Doppler plane is formulated as a figure of merit to maximize along with constant modulus constraint on the waveform. To solve the resultant non-convex problem, the sequential greedy optimization (SGO) algorithm through alternately updating the transmit waveform and receive filter is proposed. Each iteration of the developed algorithm invokes coordinate descent (CD) framework and a Lagrangian based algorithm to obtain the transmit waveform and receive filter, respectively. The analytical guarantee of the monotonic SINR improvement is proved. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

In coherent radar system, maneuvering target with complex motions usually confronts the issues of range cell migration (RCM) and Doppler frequency spread (DFS) within the long-time coherent integration process. To address these issues and achieve reliable detection and estimation performance, this paper proposes a novel fast approach without motion parameters searching. Particularly, the proposed approach performs the third-order keystone transform (TKT) to eliminate the third-order RCM (TRCM). Then, the bilinear parametric symmetric self-correlation function (BPSSF), scaled Fourier transform (SCFT) and inverse Fourier transform (IFT) are successively operated to estimate target's velocity and acceleration. Afterwards, the improved second-order phase differentiation (ISPD) is performed to estimate target's jerk. Utilizing the estimated parameters, the compensation functions could be constructed to eliminate the remaining RCMs and DFSs as well as to obtain the energy coherent integration. By contrast, analyses of several classical approaches and the proposed one are provided concerning computational complexity, coherent integration performance, detection performance and estimation performance, which confirm that the proposed approach can balance the computational burden and the detection/estimation capabilities. Additionally, the proposed approach can avoid the effect of blind speed side-lobe (BSSL). Eventually, simulated data verification and raw data processing are conducted to evaluate the efficacy of the presented approach.

This paper deals with the real-time recognition from multiple spatial angles of concealed human motions with ultra wide band (UWB) through-the-wall radar (TWR). To conquer the performance loss incurred by diverse human motion in a single view, a multi-view real-time human motion recognition model based on ensemble learning is proposed. Specifically, we first proposes a multi-view human motion recognition model based on Stacking parallel ensemble learning algorithm, which is used to realize the real-time human motion recognition based on UWB TWR. Secondly, in order to address the irrationality of the existing accuracy evaluation criteria to evaluate the real-time motion recognition algorithm, a real-time model evaluation criterion based on normalized harmonic weighted intersection over union (NHW-IOU) is proposed. Finally, the collected multi-view human motion data are used to verify the effectiveness of the proposed algorithm. The actual measurement results show that the average recognition performance of the proposed Stacking model has improved by 14.76% compared with the single-view model, which is of great significance for using multi-view data to improve network performance. Moreover, compared with the bi-directional long short-term memory (Bi-LSTM) and Gated Recurrent Unit (GRU) models, the proposed model has better performance in accuracy and time delay.

Deep learning and radar make it feasible to automatically recognize human activities in various lighting conditions, even occlusion case, which significantly promotes the application of activity recognition in the fields of security surveillance, health care, and so on. In this paper, an approach for human activity recognition (HAR) using deep learning is proposed based on stepped frequency continues wave (SFCW) radar. Specifically, SFCW radar is utilized to generate two types of characteristic representation domains, namely multiple frequencies of spectrograms in time–frequency domain and range maps in range domain. On the one hand, spectrograms and range maps provide different types of features. On the other hand, multi-frequency spectrograms furnish same type of features while with different scattering properties and frequency resolutions. Then a specific deep learning network including multiple parallel deep convolutional neural networks (DCNNs) and a sparse autoencoder is designed to extract and fuse these features associated with human activities from the multi-frequency spectrograms and rang map. In particular, each DCNN is aimed at extracting the detailed micro-Doppler features from a spectrogram, while sparse autoencoder learns prime range distribution features by compressing each range map to reduce complexity and improve robustness. Experimental results verify that the proposed deep learning scheme achieves 96.42% recognition accuracy about six types of activities by incorporating three frequencies of spectrograms and range map, and surpasses two existed methods depending on single-frequency spectrogram and combination of single-frequency spectrogram and range map.

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.

In this paper, we consider the multi-frame integration (including intra-frame integration and inter-frame integration) problem for the detection of weak moving targets, where range walk effect (RWE) occurs within the integration time. A novel integration method is proposed to achieve the accumulation of a target's multi-frame radar echoes. More specifically, the proposed integration method contains two main steps, where the modified Radon Fourier transform (RFT) is firstly used to realize the intra-frame coherent integration for every frame of echo signals. After that, the RFT-domain integration algorithm is presented to achieve the inter-frame coherent integration based on the characteristics of RFT outputs, during which the accumulation of target energy distributed in multiple frames could be realized. The explicit expressions and analysis for various performance measures of the proposed method, such as integration output response, probability of detection, input-output signal-to-noise ratio (SNR), and blind speed sidelobe (BSSL) response are derived and given. Simulation experiments are given to demonstrate the effectiveness of the proposed method.

This paper deals with the multiple targets localization problem via multi-channel ultra-wideband (UWB) imaging radar non-line-of-sight (NLOS) signal processing. A novel matching-based radar imaging algorithm is proposed to obtain the positions of multiple targets in the L-shaped corner scenario with complex multipath ghost signals. Firstly, a multipath propagation model for the multiple targets scenario is established. Then the positions of the actual multipath ghosts are extracted from the radar image, and the candidate targets corresponding to these multipath ghosts are derived. Secondly, the ellipse-cross-localization method is proposed to obtain the positions of the candidate multipath ghosts, followed by two defined matching factors to measure the similarity between actual and candidate multipath ghosts. According to the similarity, decision rules are designed to determine the actual targets. Compared with the localization algorithm based on one-dimensional range profile, the proposed algorithm can effectively cope with the cases of multiple targets, even in the cases of rough walls and noise. Finally, simulations and experimental data are used to validate the effectiveness of the proposed algorithm.

In this article, we propose a new algorithm that designs a transmit beampattern for multiple-input multiple-output (MIMO) radar considering coexistence with other wireless systems. This design process is conducted by minimizing the deviation of the generated beampattern (which in turn is a function of the transmit waveform) against an idealized one while enforcing the waveform elements to be constant modulus and in the presence of spectral restrictions. This leads to a hard nonconvex optimization problem primarily due to the presence of the constant modulus constraint (CMC). In this article, we exploit the geometrical structure of CMC, i.e., we redefine this constraint as an intersection of two sets (one convex and other nonconvex). This new perspective allows us to solve the nonconvex design problem via a tractable method called iterative beampattern with spectral design (IBS). In particular, the proposed IBS algorithm develops and solves a sequence of convex problems such that constant modulus is achieved at convergence. Crucially, we show that at convergence the obtained solution satisfies the Karush–Kuhn–Tucker conditions of the aforementioned nonconvex problem. Finally, we evaluate the proposed algorithm over challenging simulated scenarios, and show that it outperforms the state-of-the-art competing methods.

This article considers the dual-use unimodular signal design for a novel dual-functional radar-communication (DFRC) architecture. The information of downlink communication is modulated via the ambiguity function (AF) sidelobe nulling in the prescribed range-Doppler cells. At the same time, the sidelobe around the AF mainlobe is suppressed to ensure the radar detection performance. User equipment (UE) computes the AF of the received signal to make a judgment of the position of nulling for achieving demodulation. To this end, an objective function to evaluate the depth of nulling in AF is developed as figure of merit accounting for the worst-case sidelobe level ratio over the range-Doppler cells of interest. The resultant design is very challenging to solve due to the non-convex and non-smooth quartic fractional objective function and the NP-hard constant modulus constraint. Herein, we develop a fractional-alternating direction penalty method (FADPM) algorithm that invokes the fractional program theory and the ADPM framework. Specifically, we formulate a new ADPM form through introducing an auxiliary variable and resorting to the Dinkelbach's procedure. In each iteration, we transform the original quartic fractional program into two quadratic optimization subproblems both of which are approximated successively through a series of convex subproblems. We also provide the analytical convergence guarantee of the proposed FADPM algorithm. The simulation results verify the performance of the proposed algorithm and exhibit the effectiveness of the DFRC framework in modulation and demodulation process guaranteeing that a high data rate is achievable.

This paper proposes a pulse compression method by using the offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OQAM-OFDM) signal without inserting any cyclic prefix (CP). A modified interference approximation-based pulse compression method (MIA-based PCM) is proposed, by exploiting the demodulated real weights of the OQAM-OFDM signal, the inter-symbol and inter-subcarrier interferences to enhance the performance. The MIA-based PCM achieves approximated inter-range-cell interference (IRCI)-free pulse compression and perfect Doppler estimation, which has both the benefits of power efficiency and good Doppler tolerance. Finally, numerical simulation results are provided and discussed.

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.

For the reason of target multipaths ambiguities, localization for hidden moving target due to lack of line of sight (LOS) path in urban environments is a hard task. To handle this problem we propose a localization algorithm based on the internal connection of these multipaths in this paper. First, we establish an electromagnetic (EM) propagation model based on the characters of the measured signal received by an L-band single-input single-output (SISO) ultra-wideband (UWB) radar. After that, the model-based target localization algorithm is proposed based on a times-of-arrival (TOAs) association method, which is able to match the measured TOAs with the corresponding EM propagation paths correctly. The proposed method can cope with the case while some missing detections of TOAs happen. Simulation and experimental results ultimately validate the robustness and the state-of-the-art performance of the proposed algorithm.

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.

Detection and localization of nonline-of-sight (NLOS) hidden target in complex urban environment is a very recent radar detection problem. In this article, we investigate the possibility of locating a target in a building corner NLOS region with a single-input single-output ultra-wideband radar by exploring the electromagnetic (EM) diffraction propagation path. The EM wave diffraction phenomenon referring to this single rectangular corner of the building is analyzed and the diffraction model in this scenario is established. Assuming that the geometric structure of the scenario is known earlier, we introduce an application of the destination location behind the corner based on the propagation of the EM wave diffraction. Finally, the numerical simulation and real data results demonstrate that a micromotion target and moving target can be localized exploiting the proposed approach.

This paper deals with the joint design of Multiple-Input Multiple-Output (MIMO) radar transmit waveform and receive filter to enhance multiple targets detectability in the presence of signal-dependent (clutter) and independent disturbance. The worst-case Signal-to-Interference-Noise-Ratio (SINR) over multiple targets is explicitly maximized. To ensure hardware compatibility and the coexistence between MIMO radar and other wireless systems, constant modulus and spectral restrictions on the waveform are incorporated in our design. A max-min non-convex optimization problem emerges as a function of the transmit waveform, which we solve via a novel polynomial-time iterative procedure that involves solving a sequence of convex problems with constraints that evolve with every iteration. The overall algorithm follows an alternate optimization over the receive filter and transmit waveform. For the problem of waveform optimization (which is our central contribution), we provide analytical guarantees of monotonic cost function improvement with proof of convergence to a solution that satisfies the KarushKuhnTucker (KKT) conditions. We also develop extensions that address the well-known waveform similarity constraint. By simulating challenging practical scenarios, we evaluate the proposed algorithm against the state-of-the-art methods in terms of the achieved SINR value and the computational complexity. Overall, we show that our proposal outperforms state of the art competing methods while providing the most favorable performance-complexity balance.

It is often difficult to detect the targets of interest via the line-of-sight (LOS) signals in the urban environment, since the LOS signals might be not available because of the presence of the walls. This article considers the target positioning problem via non-LOS signals using the single-input multiple-output small aperture millimeter wave radar. Assume that a target is located around a building corner and the geometric structure is known a priori . The phase comparison based algorithm is proposed, by exploiting the multipath propagation of the electromagnetic wave and the phase differences of the echoes among multiple channels. Finally, we validate the proposed algorithm via real data.

In this article, we consider the problem of beampattern synthesis with sidelobe control using constant modulus weights. We specifically focus on two applications, i.e., notching and sidelobe-level (SLL) control (SLC). The considered problem is a challenging NP-hard due to the employed constant-modulus weights. Here, we convert the problem into an intermediate form by incorporating auxiliary variables and changing the form of the constant modulus. Then, we decompose the intermediate form into subproblems of single constraint each. Finally, we propose an iterative solution following the alternate direction method of multipliers (ADMM) and Proximal frameworks. The proposed solution solves the original problem by iteratively solving the subproblems in coordinated logical order. Importantly, it combines the convergence speed of ADMM and convergence property of Proximal methods. In addition, it always satisfies the constant modulus constraint. The solution has a significant contribution to mitigate interference using notching and to realize RadCom. Simulation results show that the proposed solution has a better performance in terms of computational speed, notching, and constant SLL than the previous works.

Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this article addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition.

In human activity recognition (HAR) based on radar, significant research exists on statistical features extracted from the spectrogram (μD), whereas the research which considers other domains is less developed. This paper is aimed to investigate three domains of radar data: μD, Cadence Velocity Diagram (CVD), and range-time (RT) information, evaluating which ones are best suited to classify specific activities. In addition, information fusion is applied to enhance classification accuracy and compare it with the results of single domain approach. Based on the previous results, a hierarchical structure is proposed to improve the performance of classification further. The preliminary results show that different domains have distinctive sensitivity to specific activities. RT information is sensitive to the moving target crossing range bins, while CVD is more sensitive to body movement. The μD is more balanced, which means it can observe both moving targets and body movements. Furthermore, improvement in accuracy is approximately 6-23 % using feature-level fusion. A hierarchical classification approach is also investigated, which has accuracy in the order of approximately 92 %.

This study deals with a tracking problem for hidden human targets using time-division multiple-input-multiple-output through-wall imaging radar (TWIR). An efficient image-domain tracking algorithm is proposed. Specifically, the authors first utilise back-projection algorithm and the phase coherence factor (PCF) to obtain multi-frame high quality images. Then a tracking algorithm via amplitude-distribution-based particle filter is proposed. Experimental data validates that this algorithm has a commendable effectiveness for hidden target tracking.

The problem of adaptive target detection in compound-Gaussian clutter with unknown covariance matrix for multiple-input multiple-output (MIMO) radar is addressed in this paper. A set of secondary data for each receiver is assumed to be available, and the primary data and the secondary data own the same covariance matrix structure but different power levels (textures). Firstly, a Bayesian approach is proposed, where the structure is modeled as a random matrix with an appropriate distribution. Then, two ways are adopted to model the texture: an unknown deterministic quantity or a random variable ruled by certain distribution. In this framework, three adaptive generalized likelihood ratio tests (GLRTs) are developed using the two-step design procedure. Finally, the capabilities of the proposed detectors and their superiority with respect to some existing techniques are evaluated via numerical simulations.

Here we consider a corner target positioning problem under the condition of unknown walls' positions using time-division multiple-input-multiple-output (MIMO) radar. Firstly, we propose an estimation algorithm for the walls' positions using the Back-Projection (BP) imaging algorithm. Then, after obtaining the BP image of target, we extract the focus regions produced by different multipaths. Finally, we obtain the position of the target based on the extracted walls' positions and the focus regions. Simulations and experiments results validate the proposed algorithm.

In the presence of the mainlobe jamming, the performance of the modern radar system would degrade significantly, i.e. increasing probabilities of false alarm and loss detection. In this study, the authors consider the jamming suppression problem in noise environment for the distributed radar with single transmitter and multiple receivers, where the multiple jammings enter into all the receivers through the main beam of the antennas. A framework based on joint blind source separation (JBSS) is proposed. First, the signal model accounting for both the target and jammings is developed. Second, the target and the jamming signals are separated by exploiting the generalised non-orthogonal joint diagonalisation JBSS method. Then, the separated target signals are employed to find the target location with the elliptic location method. Finally, they assess the performance in noise environment by numerical simulations, and the results show that the proposed algorithm can suppress successfully quite a lot different kinds of jamming signals, e.g. the noise-modulated and the digital radio-frequency memory jammings.

We develop a novel radar-based human motion recognition technique that exploits the temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with long short-term memory (LSTM) units is employed to extract sequential features for automatic motion classification. The spectrogram of raw radar data is used as the network input to utilize the time-varying Doppler and micro-Doppler signatures for human motion characterization. Based on experimental data, we verified that a stacked RNN with two 36-cell LSTM layers successfully classifies six different types of human motions.

The detection and tracking of human targets behind the wall is of great importance in urban sensing. The random and high maneuvering behaviors of moving human targets and the clutter diversity lead to high missed detection and false-alarm probability. In this paper, we consider two-dimensional human-target tracking problem, and a multi-algorithm fusion (MAF) framework exploiting the mean-shift algorithm and the Kalman filter is proposed. Compared with the mean-shift algorithm, the proposed MAF framework has robust tracking performance, especially in the presence of multiple targets. Finally, the proposed MAF framework is evaluated by simulations and real data.

Long-time coherent accumulation can effectively improve the detection and motion parameters estimation ability of radar for maneuvering weak target. Nevertheless, the range cell migration (RCM) and Doppler spread caused by high-speed maneuvering target may make the detection and estimation performance to degrade seriously. In this paper, a new coherent integration (CI) approach based on modified axis rotation transform (MART) and Lv’s transform (LVT), i.e., MART-LVT, is proposed to achieve the detection and parameter estimation of target with high velocity and acceleration motion. The MART-LVT method can correct RCM via rotating the coordinate locations of echo data and obtain the CI and parameters estimation of target signal via LVT. In comparison with the existing representative coherent detection methods, MART-LVT can achieve better integration performance and detection probability. Simulations and real data demonstrate the effectiveness of MART-LVT.

This paper deals with the robust waveform design of multiple-input multiple-output radar to improve target detectability embedded in signal-dependent interferences. Two iterative algorithms with ensuring convergence properties are introduced to maximize the worst case signal-to-interference-plus-noise ratio (SINR) over steering matrix mismatches under the constant modulus and similarity constraints. Each iteration of the proposed algorithms splits the high-dimensional problem into multiple one dimensional problems, to which the optimal solutions can be found in polynomial times. Numerical examples are provided to assess the capabilities of the proposed techniques in comparison with the existing methods in terms of the SINR and the computational times for both the continuous and discrete phase cases of the probing signal.

This study deals with the problem of range-velocity joint deception jamming suppression for a single-input multiple-output (SIMO) radar system. The Doppler diversity and spatial geometric correlation are exploited to distinguish the jamming and multiple moving targets. First, the echoes in each receiver are mapped into range-Doppler plane by pulse compression and slow-time Fourier transform operations. Then, in each receiver, the range and Doppler frequency of the targets are extracted. Third, based on differences between the physical target echoes and the jamming signals in both range and Doppler dimensions, two identification algorithms are developed to discriminate the false targets in fusion centre. Finally, through numerical simulations, the performance of the proposed suppression scheme is evaluated, showing its capability to suppress the range-velocity joint deception jamming and locate the multiple physical targets with high precision.

We study the spectral constrained design of Multiple-Input Multiple-Output (MIMO) radar beampattern in an effort to the coexistence with the communication systems. We develop an optimization model relying on the minimization of beampattern Integrate-Sidelobe-Level (ISL) along with spectral, mainlobe width, Peak-to-Average Ratio (PAR) and energy constraints. To cope with the resultant non-convex problem, we introduce a novel polynomial-time iterative procedure that requires solving a series of constrained convex problems that evolve with each iteration. In particular, the proposed algorithm ensures that the monotonic decrease of ISL and the convergent solution is a KarushKuhnTucker (KKT) point. Numerical results are provided to assess the proposed algorithm in terms of the beampattern and spectral compatibility.

To solve the degradation of clutter suppression effect of traditional radar signal processing method in the complex battlefield environment, especially for the airborne radar with more complex clutter background, in this paper, we use Conditional Generative Adversarial Nets (CGAN) to train a generator which can find interested targets in a complex clutter distribution. It exploits the distribution of interested targets as supervised information so as to assist the extraction of high-resolution features. Moreover, for the time-varying battlefield environment, only by updating model parameters can our method adapt the change of distribution of radar echo data. The effect of our method on clutter suppression is validated based on the experiments on the radar datasets.

Deep learning has attracted intensive attention in human activity classification based on the radar. Whereas, most methods use the images to classify the human activities, ignoring the phase information of the radar data. In this paper, the complex-valued convolutional neural network (Complex-valued CNN) is utilized to classify the human activity behind the wall. We developed several Complex-valued CNN models, which have the same structures as several classical convolutional neural network(CNN) models and use both the amplitude and phase information of the range profiles. Experiments on the real data validate the performance of the Complex-valued CNN models.

We proposed a robust real-time human activity recognition method based on attention-augmented Gated Recurrent Unit (GRU) using radar range profile, namely Attention-Augmented Sequential Classification (AASC). We use attention mechanism to capture the temporal relationships inherent in the range profile signatures. Therefore, our model can learn long-term temporal correlation of human activity without increasing the depth or width of recurrent neural network. The attention weights are adaptively generated using features extracted by the GRU recurrent neural network. Finally, attention-augmented features are classified by Multi-layer perceptrons. Real data of picking, boxing, rasing leg and rasing hand are collected to evaluate our model. It is shown that the proposed method outperforms the conventional GRU in recognition accuracy and robustness, demonstrating the superiority in real-time activity recognition task.

This paper deals with the design of the Doppler filter bank for non-uniform pulse repetition interval (PRI) radar in the presence of signal-dependent clutter. The worst-case Signal-to-Interference-Noise-Ratio (SINR) at the output of the Doppler filter bank is explicitly maximized along with the mainlobe gain restriction. To solve the resultant non-convex problem, a Parallel Block Improvement (PBI) algorithm is proposed to monotonically increase the worst-case SINR to convergence. In particular, each block involves a non-convex problem associated with the each filter, which is approximated through a series of convex problem. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

This paper considers the coherent integration problem for moving target detection in phased-Multi-In Multi-Out (MIMO) radar. An integration method based on entropy is proposed to realize the coherent accumulation of target signal energy of the multi-subarrays for the phased-MIMO radar. Firstly, the two dimension (fast time-slow time) signal model is established. Secondly, the differences of signal phase along the fast time dimension are eliminated via frequency shift operation after range alignment, while the Doppler frequency induced by target’s velocity is then compensated using slow time Fourier Transform (FT). Thirdly, the phase differences caused by the time delay are estimated and compensated by using an entropy-based approach. Finally, the Doppler-location searching is presented to achieve the coherent accumulation. Simulation examples are given to demonstrate the effectiveness of the proposed method.

MIMO through-wall-radar is traditionally utilized to perform forward-view imaging (FVI) for the hidden targets behind the wall. Due to the limited view angle and target occlusion, FVI usually suffers from the problem of target missing in the case of multiple targets. In this paper, a novel down-view imaging (DVI) mode is presented to obtain robust three-dimensional (3D) multiple target images without target missing. Specifically, the propagation characteristic is analyzed for the 3D DVI in an enclosed building space. Then the DVI algorithm is introduced. Specifically, the back-projection algorithm is applied to form a 3D image and the exponential phase coherence factor (EPCF) weighting is adopted to suppress the multi-path ghosts. Based on the gprMax simulation results, in the presence of target occlusion, it is demonstrated that the presented DVI method has the ability to implement robust imaging for multiple targets, while the FVI misses the obscured target.

This paper deals with the joint design of transmit waveform and receive filter improving radar Signal to Interference plus Noise Ratio (SINR) in signal-dependent interference and spectrally contested-congested environments. The injected interference energy on each common band is precisely controlled for spectral compatibility. Besides, the transmit signal complies with constant envelop and similarity constraints to bestow some attractive waveform characteristics. To handle the resulting transceiver design problem, an iterative optimization procedure with a polynomial computational complexity is developed leveraging the Coordinate Descent (CD) framework. Finally, numerical results are provided to highlight the effectiveness of the proposed joint transmitter and receiver design technique.

Human activity recognition (HAR) has long been a research hotspot in anti-terrorism, patient monitoring and other applications. Throughout the current research progress, the realtime recognition of unknown start and end time has not been well solved. In addition, the recognition accuracy of blocking human in the wall-through scene needs to be improved. To tackle this issue, this paper proposed a novel range profile sequence driven end-to-end model, which specifically employed random cropping training method. Then, we carried out experiments on actual Through-the-Wall Radar(TWR), finally achieved an average accuracy of 97.6% on four common activities, and the most significant is that our method can immediately output recognition results, without waiting for the end of activity.

This paper deals with the joint design of transmit waveform and receive filter to improve the clutter rejection capability for non-uniform pulse repetition interval (NUPRI) airborne radar. Specifically, a multipulse echo model accounting for a moving point-like target and signal-dependent clutter is first established. Then the echo is processed via the matching filter and the windowed Doppler filter bank to obtain the two-dimensional range-Doppler plane. Further, the integrated sidelobe level of clutter (ISLC) that spreads the region of the target of interest in the plane is considered to minimize forcing constant modulus constraint on the waveform. To solve the resultant nonconvex problem, the Sequential Greedy Optimization Algorithm (SGOA) through alternately updating the receive filter and transmit waveform is proposed to monotonically decrease ISLC to converge. In each iteration, the iterative algorithm based on coordinate descent (CD) framework and the sequential convex approximation algorithm are, respectively, explored to obtain the transmit waveform and receive filter. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

This paper studies the unimodular sequence design with the finite alphabet case for a cognitive radar in order to achieve the desired auto-correlation. An iteration algorithm based on Inexact Alternating Direction Penalty Method (IADPM) framework is developed to minimize the Weighted Integrated Sidelobe Level (WISL). In each iteration, it splits the NP-hard problem into two subproblems via an introduced auxiliary variable, while locally increasing the penalty factor involved in the IADPM framework. The proposed algorithm is shown to converge for any initialization under some mild conditions and avoids the non-convergence problem of ADMM when handling the NP-hard problems. Finally, the auto-correlation performance and convergence speed of the proposed algorithm are evaluated against the state-of-the-art methods. Results show that our proposal outperforms the state-of-the-art competing methods while providing the favorable performance-complexity balance.

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.

This paper deals with the robust joint design of a 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 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 study proposes a new radar-based human body and limb motion recognition method that exploited the temporal sequentiality of the motions. A stacked gated recurrent units network (SGRUN) is adopted to extract the dynamic sequential human motion patterns. Since the time-varying Doppler and micro-Doppler signatures can commendably represent such motion patterns, the spectrogram is utilised as the input sequence of the SGRUN. Numerical experiments verify that an SGRUN with two 34-neuron gated recurrent unit layers well classifies and recognises six distinct human body and limb motion types.

High-speed target often suffers from the range walk (RW) problem within the process of long-time coherent integration (CI). The aim of this paper is to present a new coherent detection algorithm, i.e., modified location rotation transform (MLRT), which could effectively correct the RW and estimate the velocity of the high-speed target. In particular, the MLRT algorithm could rotate the coordinate locations of echo data to remove the RW and estimate the velocity via searching the rotation angles. Then, the CI of target's energy is obtained via the Fourier transform along the slow-time direction. The advantage of the MLRT is that it could achieve superior integration and detection performance and avoid the blind speed sidelobe effect without increasing the computational complexity, in comparison with other representative algorithms. Simulations and real data processing are provided to evaluate the efficacy and feasibility of the proposed algorithm.

This paper considers the constrained waveform design for Multiple-Input Multiple-Output (MIMO) radar to synthesize a desired beampattern. Specifically, resorting to SemiDefinite Programming (SDP) related technique, we first minimize Integration Sidelobe Level (ISL) to optimize the waveform covariance matrix enforcing a uniform elemental power restriction as well as a 3dB bandwidth constraint. Then, based on Least Square (LS) approach, we present the existing Cyclic Algorithm (CA) and a new Sequential Iterative Algorithm (SIA) to devise the waveform under a constant modulus constraint, and a similarity constraint to allow the designed waveform sharing the similarity feature with a given reference waveform. In particular, the proposed SIA directly optimizes the objective function and its each iteration turns the multidimensional optimization problem into multiple one-dimensional optimization problems with closed-form solutions. Finally, we assess the effectiveness of the proposed technique through numerical simulations in comparison with CA.

This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called “clustering-CFAR detector,” is data dependent and composed of three stages: an adaptive clustering procedure that, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector that chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of “clustering-CFAR detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection.