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Sparsity_Driven_Moving_Target_Detection_in_Distributed_Multistatic_FMCW_Radars.pdf
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We investigate the problem of sparse target detection from widely distributed multistatic \textit{Frequency Modulated Continuous Wave} (FMCW) radar systems (using chirp modulation). Unlike previous strategies e.g., developed for FMCW or distributed multistatic radars), we propose a generic framework that scales well in terms of computational complexity for high-resolution space-velocity grid. Our approach assumes that the target signal is sparse in a discrete space-velocity domain, hence allowing for non-static target detection, and the resulting multiple baseband radar signals share a common support. By simplifying the representation of the FMCW radar signals, we propose a versatile scheme balancing complexity and detection accuracy. In particular, we design a low-complexity, factorized alternative for the Matching Pursuit algorithm leveraging this simplified model, as well as an iterative methodology to compensate for the errors caused by the model simplifications. Extensive Monte-Carlo simulations of a K-band radar system show that our method achieves a fast estimation of moving target's parameters on dense grids, with controllable accuracy, and reaching state-of-the-art performances compared to previous sparsity-driven approaches.
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Monnoyer de Galland de Carnières, G., Feuillen, T., Jacques, L., & Vandendorpe, L. (2019). Sparsity-Driven Moving Target Detection in Distributed Multistatic FMCW Radars. in Proceedings of IEEE CAMSAP 2019, Le Gosier, France, December, 15-18, 2019. Published. CAMSAP19, Le Gosier, Guadeloupe, France. https://doi.org/10.1109/CAMSAP45676.2019.9022656