Target Sensing for ISAC Under Jamming

Abstract

In this paper, we investigate moving target sensing for integrated sensing and communication (ISAC) systems in the search stage in the presence of clutter and noise-like jamming. We first derive the Cramér-Rao lower bound (CRLB) for the target and jammer parameters under the considered observation model. Then, we develop a sensing scheme that removes clutter, whitens the observations, and identifies angular domain target candidates through sparse recovery with local refinement. Based on the detected target candidates, we design a robust receive combiner to suppress interference leakage under angular mismatch, estimate the target range and velocity by a coarse-to-fine maximum likelihood (ML) algorithm, and classify the angular candidates under the target and noise-like jamming models by a generalized likelihood-ratio test (GLRT). To further mitigate the residual jammer leakage, we refine the target parameters through ML optimization with covariance update. Simulation results show that the proposed scheme can effectively suppress clutter and jamming, improve the output signal-to-interference-plus-jamming-plus-noise ratio (SIJNR), and achieve estimation performance close to the post-projection CRLB.