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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.

This paper deals with the coherent radar detection with non-uniform pulse repetition intervals (PRI), which has strong electronic counter-countermeasures capabilities to enemy jammer thanks to its low interception and difficult to sort. A novel coherent processing framework for the non-uniform PRI returns is proposed to detect moving targets by exploiting the PRI relationship of the transmitting time sequences. It can achieve the range and velocity measurement without ambiguity. In addition, the optimized weighted window in the Doppler domain is designed to suppress the sidelobe levels which is introduced by the non-uniform PRI sequences. Finally, several simulation results are provided and discussed.

In this paper, we address the signal estimation problem for a linear combination of multiple structured models, which is widely employed in the passive and/or active sensing systems to characterize the behaviors, for example, jamming and multipath propagation, in radar and communication societies. An iterative sequential estimation (ISE) algorithm is presented to obtain simultaneously the multiple structured signals. At each iteration, employing the estimated signals at the previous step, the optimal linear filters, based on mean-squared error criteria, are designed to minimize the output average power for every element of each signal. Finally, we evaluate the performance of the proposed ISE method compared with the least-square and compressed sensing algorithms via numerical simulations. The results highlight the presented algorithm shows a better signal estimation performance at low SNR and plays a trade-off between the computational complexity and the signal estimation performance.

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.

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.

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.

For multi-input multi-output (MIMO) through-wall radar imaging (TWRI), multipath ghosts and side/grating lobe artifacts degrade the imaging quality of the obscured targets inside an enclosed building, therein hindering target detection. In this paper, an approach based on two stacked generative adversarial nets (GAN) is proposed to achieve multipath and side/grating lobe suppression with regard to MIMO TWRI. Specifically, the Stage-I GAN constructs a spatial structure mapping from the original input images to the Stage-I GAN output images with the suppressed multipath ghosts. However, the side/grating lobe artifacts are intentionally preserved in the stage-I GAN as additional constraint information to prevent uncontrollable over-fitting. Then, the Stage-II GAN takes the output images of Stage-I GAN as input to suppress the side/grating lobe artifacts. Extensive electromagnetic simulations and comparisons demonstrate that the proposed approach achieves better suppression of multipath ghosts and side/grating lobe artifacts and other significant superiorities, including priori wall information not being required, the preservation of weak targets, and robustness for different array deployments and building layouts.

This paper is focused on the design of the shared signal for a dual-use system with radar detection and communication functions. To achieve a good aperiodic autocorrelation property of the shared signal for radar detection, the lp-norm of the autocorrelation sidelobes is minimized. For a communication function, the nulling of one or more frequency bands enables information to be embedded and an energy minimization detection method is introduced to demodulate communication information. In addition, electromagnetic spectral compatibility and unimodular constraints on the designed signal are considered. The resulting problem is a higher order, multi-dimensional, and multi-objective constrained optimization, which is efficiently handled through an iteration direct search algorithm. Finally, the performance of the proposed method is assessed and analyzed through numerical simulations in terms of the peak sidelobe level, average sidelobe level, and bit error rate.

This paper deals with the problem of local ambiguity function shaping in the presence of narrow-band spectral interferences (e.g., the communication signal and jamming), which has significant applications in the radar detection field. An objective function involving the weighted integrated sidelobe level and the spectral stopband energy is developed as a figure of merit to minimize. Besides, the energy and the peak-to-average ratio restrictions are forced on the probing waveform. To handle the resulting non-convex quartic optimization problem, an iterative sequential quartic optimization algorithm sharing polynomial time complexity is proposed. In each iteration, it decomposes the original problem into two alternately tractable quadratic subproblems both of which can be converted to linear optimization problems with closed-form solutions. Finally, the numerical simulations are provided to assess the effectiveness of the proposed algorithm. The results show that the proposed algorithm outperforms some available counterparts in the aspect of the convergence speed and the capability to prohibit the narrow-band spectral interferences and the sidelobes of a strong return from masking weak targets.