GLRT-based compressive subspace detectors in single-frequency multistatic passive radar systems

The authors study the problem of compressive target detection in a single-frequency network (SFN)-based multistatic passive radar system (MS-PRS) consisting of multiple illuminators of opportunity (IOs) and one receiver. Firstly, a generalised likelihood ratio test (GLRT)-based SFN-based compressive subspace detector (SFN-CSD) is derived by exploiting the sparsity of the target echoes for the case of known noise variance. When the noise variance is unknown, an SFN-based unknown-noise (UN) compressive subspace detector is proposed, referred to as the SFN-UNCSD. Moreover, closed-form expressions of the probability of false alarm and detection of the proposed detectors are deriived. It is proved that the SNF-UNCSD has a constant false alarm rate (CFAR) property. Finally, numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors. The authors focus on the problem of compressive target detection in a single-frequency network (SFN)-based multistatic passive radar system (MS-PRS) without signal reconstruction. Having the knowledge of the subspace of the target echoes, the SFN-CSD/SFN-UNCSD for the case of known/unknown noise variance is proposed by the authors. Numerical results were conducted to verify that the proposed SFN-CSD outperforms the GCC detector, OSOMP detector and ED.image