Random matrix theory inspired passive bistatic radar detection of low-rank signals

For passive bistatic radar with a noisy reference signal, we propose a singular value decomposition (SVD) and Eigen detector that significantly outperforms the conventional cross-correlation detector. We consider the scenario when the signals of opportunity across several independent snapshots/pulses span a low-rank signal space. The target reflectivity is assumed to change independently from one pulse to another within a processing interval. We demonstrate this performance improvement through extensive numerical simulations across various surveillance and reference signal-to-noise ratio (SNR) regimes.