Search

Among various types of sensors, through-the-wall imaging radar (TWIR) is widely used in concealed targets detection and urban environment perception. Especially, multi-view TWIR has been received more and more attention in recent years for its ability to provide more accurate position estimation and reduce the blind areas. However, when exploiting the multi-view TWIR to detect targets in the complicated indoor environment with an unavailable global positioning system, the relative position of the radars is hard to be obtained and the acquired non-uniform images can not be aligned automatically. To deal with these problems, an image registration approach based on graph matching for multi-view TWIR is proposed in this paper. In this approach, we exploit an important feature of multi-view TWIR images: the invariant relative position of targets in different views. Based on this characteristic, the acquired images are modeled as complete graphs, and graph matching is employed to acquire the relative position parameters of the radars and register the images. Compared with the classic method, the proposed method can overcome the critical challenges of invalid local features as well as high-intensity ghosts, and achieve better performance for multi-view TWIR image registration. The effectiveness of the proposed method is evaluated via both simulations and real data tests.

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.

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

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 this letter, we consider the tomographic imaging issue of layouts only using the received signal strength (RSS) of dual narrowband signals. Specifically, we first introduce the signal model, which describes the relationship between RSS and the layouts in unknown scenario. Then, the propagation differences of dual-band signals are analyzed theoretically. Next, a joint iterative imaging method based on improved total variation constraint is proposed, and the arithmetic fusion strategy is used to obtain final result. Finally, the real data results demonstrate that the proposed approach with dual-band signals can retain the clear edge information of building structures and greatly reduce the artifacts in results.

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.

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 letter deals with a shaped beampattern synthesis problem through controlling the mainlobe ripple and the output filter energy based on the minimization of the peak sidelobe level in the spatial region of interest. To tackle the challenging optimization problem with nonconvex constraints, a novel first-order iterative convex approximation (FOICA) procedure that handles the nonconvex constraints with their convex upper bound functions resorting to the first-order Taylor expansion is developed. Hence, in each iteration, the original problem is approximated through a convex problem globally solved via a polynomial time method. Additionally, a nonnegative slack variable to ensure a decreasing trend in the objective function during the iteration is introduced. Finally, the numerical simulations are provided to verify that the proposed algorithm outperforms the state-of-the-art algorithms in terms of achieved beampattern performance.

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.