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This letter considers the robust spatial-filter design problem for multipath exploitation. The imprecise multipath return results in the output Signal-to-Interference-plus-Noise-Ratio (SINR) performance deterioration of traditional beamformers. We focus on enhancing the worst-case output SINR over the uncertain Direction-Of-Arrival (DOA) sector. To achieve robustness against covariance matrix errors caused by the desired signals contamination, we reconstruct the Interference-Noise Covariance Matrix (INCM) from Sample Covariance Matrix (SCM) based on the proposed residual noise components removal idea. Then, we cast the robust spatial-filter design problem as a max-min optimization problem, aiming at improving the worst-case output SINR. The filter vector is finally obtained via semi-definite relaxation followed by randomization synthesis. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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.

Multipath exploitation with compressive sensing (CS) has been successfully applied in through-the-wall radar imaging (TWRI) given prior knowledge of the room geometry. However, in most practical applications and critical missions, the geometry of the room is not known. In this paper dynamic wall pursuit algorithm is proposed to simultaneously recover the room geometry and image the scene behind the wall. We demonstrate that moving targets increase the wall reconstruction accuracy. The algorithm exploits the fact that wrong wall positions populate the reconstructed image with false targets. It makes use of the moving targets in the scene to increase the certainty of the detected wall positions. Simulated results show the effectiveness of this method even at low SNR values.

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. [ABSTRACT FROM AUTHOR]

Detection and localization in urban environments is a very recent radar problem. In this paper, we investigate the possibility of detecting and locating targets not in direct line of sight (NLOS) areas with a single portable radar by exploiting multipath returns. We propose two algorithms, which handle the information provided by multipath returns in different ways to detect and estimate the NLOS target position. We also present an original method to select the number of paths to take into account in the algorithms in order to maximize detection probabilities. Numerical results show good efficiency of the proposed algorithms for problems of both detection and localization. We show that applying these algorithms improves detection performance compared to a classic matched filter in a typical urban scenario. Experimental results on a real dataset allow us to validate our multipath model in urban environments, and in particular to show that it is possible to retrieve the target location even with rough knowledge of the scene geometry. [ABSTRACT FROM AUTHOR]

This paper considers the multipath modeling and exploitation problems for multiple input multiple output through-the-wall imaging radar. A multipath propagation model referring to two parallel walls is established, and the position distribution characteristics of the multipath ghosts in radar image are analyzed by means of geometric approximation. Therein, we find a feature that the locations of the walls can be obtained using the geometric relationship between the true target and its multipath ghosts. After utilizing constant false alarm rate technique and clustering approach, the coordinates of the multipath ghosts and the concealed target are achieved, and the positions of the two walls are obtained. Finally, simulation and experimental results validate the correctness of the multipath model and the validity of the multipath-exploitation-based wall localization method. [ABSTRACT FROM AUTHOR]

Localization using the time-of-arrival (ToA) of a wideband signal has the potential to achieve high accuracy, thereby making it a promising choice for a variety of applications. However, a major challenge impacting localization accuracy is multipath propagation, i.e., the existence of indirect paths from a target to an anchor (transceiver) via one or more obstacles present in the environment. In this paper, we provide an overview of the techniques proposed in the literature to analyze and address the effects of multipath on localization accuracy. For this purpose, we first cast the localization of one or more targets as a maximum a priori estimation problem, where the distribution of the amplitudes and ToAs of the indirect paths serves as a prior. Under this framework, we show that multipath can either be a blessing or a curse depending on the extent of prior knowledge available about the multipath statistics. Specifically, in the absence of any indirect path information, only the direct paths that go straight from a target to an anchor contain useful position information; in other words, multipath negatively impacts localization performance. Thus, in this case, it is important to detect the anchors having line-of-sight to the target(s), and we review the techniques reported in the literature to solve this problem. On the other hand, if complete indirect path information is available (i.e., the propagation paths of all the multipath components are known), then each indirect path is equivalent to a direct path from a virtual anchor, and hence, the spatial diversity offered by multipath can be exploited for improving localization accuracy. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS)-based multipath exploitation has been successfully applied to stationary indoor scenes in through-the-wall radar imaging (TWRI). The benefits of using significantly reduced data are also desirable for moving targets. Hence, we bring CS based multipath exploitation to the non-stationary target domain and are able to treat moving and stationary targets simultaneously. In general, multipath propagation has adverse effects on the image quality. However, by using proper modeling, multipath can be used to one’s advantage. In this paper, we apply CS to both stationary and moving targets under interior wall scatterings. Assuming knowledge of the room geometry, we develop an effective method that solves the inverse problem of joint localization and velocity estimation of the targets in an indoor multipath environment from a few measurements. We also propose a computationally inexpensive scheme that first locates the targets using sparse reconstruction and subsequently estimates the velocity vectors. Effectiveness of the proposed methods is demonstrated using both simulated and experimental data. [ABSTRACT FROM PUBLISHER]

In this letter, we propose a new algorithm to determine the position of the sidewalls by exploiting the multipath echoes of a target bounced from the sidewalls, which is useful to obtain the building layout, determine the relative position of the target in the room, and remove the higher order multipath ghosts for a through-wall tracking radar. Specifically, we first extract the 1-D trajectories of the real target and the multipath ghosts in each receive channel, based on the local maximum values extraction method and the 1-D Kalman filter. Second, by exploiting the principle of the first-order multipath echoes, the position of the sidewall is computed in each frame. In addition, the sidewall position can be obtained by averaging the coordinates of the target and the ghost in the presence of the second-order multipath echoes. Finally, the average of multiple frames is adopted to improve the detection accuracy. The proposed algorithm is validated by simulation and experimental data. [ABSTRACT FROM PUBLISHER]

We derive a multipath model for sensing through walls using radars. The model considers propagation through a front wall and specular reflections at interior walls in an enclosed room under surveillance. The model is derived such that additional eigenrays can be easily accommodated. A synthetic aperture radar (SAR) system is considered, and stationary or slowly moving targets are assumed. The focused downrange and crossrange locations of multipath ghosts are established and validated using numerical, as well as experimental data. The multipath model permits an implementation of a multipath exploitation algorithm, which associates, as well as maps, each target ghost back to its corresponding true target location. In doing so, the proposed algorithm improves the radar system performance by aiding in ameliorating the false positives in the original SAR image, as well as increasing the signal-to-clutter ratio at the target locations, culminating in enhanced behind the wall target detection and localization. [ABSTRACT FROM PUBLISHER]

We propose a simple three-dimensional (3-D) direction-finding system that exploits multipath reflections close to the sensors to improve performance by increasing the effective array aperture. Such close-range multipath reflections can originate, for example, from parts of the platform on which the sensor is mounted. Our system is inspired by the human auditory system where multipath reflections from the external ear (pinna) enable 3-D direction finding. To demonstrate the advantage of exploiting multipath reflections, we consider a simple example of a passive system with only one sensor and two nearby reflectors. First, we present a parametric measurement model and then compute the asymptotic Cramer-Rio bound on the 3-D direction estimates for a stochastic source signal of unknown parametrized spectrum. We provide a few numerical examples to illustrate our analytical results. We find that for this example additional reflectors and a wider source spectrum can achieve better direction estimates. [ABSTRACT FROM AUTHOR]