Detection and localization in urban environments is a very recent radar problem. In this article, we investigate the possibility of detecting and locating targets in 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 data set 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.

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In this letter, we consider the robustness of the mismatched filter in presence of off-grid target. Since for an offgrid target, the received sampled signal differs from the on-grid signal, the mismatched filter as optimized in the literature is not optimal anymore, and in particular may present a deteriorated sidelobe level. We propose here a general optimization program enabling to get an optimal mismatched filter -with respect to the Integrated Sidelobe Level (ISL) -within the resolution cell. We detail then the resulting solution in two particular radar cases, namely a Doppler steering vector and a linear chirp signal. In both cases, the proposed solution involves the Discrete Prolate Spheroidal Sequences. Simulation results show that this solution indeed provides the best ISL in the resolution cell.

Unresolved scatterers (separated by less than a 3 dB matched filter main lobe width) are known to degrade the matching pursuit performances in radar: it tends to generate spurious detection or miss weaker targets, hidden in strong sidelobes. In this article, we propose a new matching pursuit algorithm performing a subspace radar resolution cell rejection. The philosophy is the following: as it is usually not possible to distinguish several unresolved scatterers, we do not try to distinguish them, but when a scatterer is detected, then every contribution in the corresponding resolution cell should be cleaned. For that, the continuous cell interval is approximated by a subspace and the corresponding orthogonal projector is build. The projector basis is chosen so that it minimizes the projection residue inside the interval and a method is proposed to select its dimension according to the rejected target SNR. We show that it enables to control the sidelobe level after rejection (orthogonal projection), so that the sidelobe detection probability can be maintained low. It allows matching pursuit to work well even if the target parameter distribution differs from a delta Dirac distribution inside the matched filter resolution cell.