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Sparse Reconstruction in Co-Pulsing and Co-STAP FDA Radar

The University of Maryland, College Park

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Target localization based on frequency diverse array (FDA) radar has lately garnered significant research interest. A linear frequency offset (FO) across FDA antennas yields a range-angle dependent beampattern that allows for joint estimation of range and direction-of-arrival (DoA). Prior works on FDA largely focus on the one-dimensional linear array to estimate only azimuth angle and range while ignoring the elevation and Doppler velocity. However, in many applications, the latter two parameters are also essential for target localization. Further, there is also an interest in radar systems that employ fewer measurements in temporal, Doppler, or spatial signal domains. We address these multiple challenges by proposing a co-prime L-shaped FDA, wherein co-prime FOs are applied across the elements of an L-shaped co-prime array and each element transmits at a non-uniform co-prime pulse repetition interval (C3 or C-Cube). This co-pulsing FDA yields significantly large degrees of freedom (DoFs) for target localization in the range-azimuth-elevation-Doppler domain while also reducing the time-on-target and transmit spectral usage. By exploiting these DoFs, we develop a C-Cube auto-pairing (CCing) algorithm, in which all the parameters are ipso facto paired during a joint estimation. We benchmark the performance of this new radar configuration by deriving lower error bounds and theoretical guarantees. Next, we examine range-dependent clutter suppression for co-pulsing radar via space-time adaptive processing (Co-STAP). Here, we propose an approximate method of three-dimensional (3-D) clutter subspace estimation leveraging the well-known discrete prolate spheroidal sequences (DPSS) to make a trade-off between the clutter suppression performance and computational cost. Compared to the conventional FDA-STAP algorithm, the proposed DPSS-based method for Co-STAP exhibits the merits of better clutter suppression performance, lower computational complexity, and robustness to interference.