Multistatic passive detection with parametric modeling of the IO waveform

This paper examines the target detection problem for a passive multistatic radar employing illuminators of opportunity (IOs), where the receivers are contaminated by non-negligible noise and direct-path interference (DPI). A parametric approach is proposed by modeling the unknown signal transmitted from the IO as an auto-regressive (AR) process whose temporal correlation is jointly estimated and exploited for passive detection. The proposed solution is developed based on the generalized likelihood ratio test principle, which involves non-linear estimation that is solved by using the expectation-maximization (EM) algorithm. We also discuss the initialization of the EM algorithm and the joint adaptive model order estimation for the AR process without using any training signal. In addition, we extend several conventional passive detectors, which were introduced by assuming no DPI is present, to provide them with an ability to handle the DPI problem. A clairvoyant matched filtering (MF) detector is derived as well assuming the knowledge of the IO waveform. Extensive simulation results are presented, using simulated waveforms whose temporal correlation can be easily controlled, as well as practical IO waveforms transmitted by frequency modulation (FM) radio. The results show that the proposed EM-based passive detector outperforms conventional passive detectors due to the exploitation of the waveform correlation.