Direct Target Localization for Distributed Hybrid Active-Passive Radars With Direct-Path Interference

Distributed hybrid active-passive radars (HAPRs) with separately deployed transmitters and receivers face localization performance limitations due to the inevitable presence of direct-path interference (DPI). To improve target localization accuracy and enhance DPI suppression, we develop a direct position estimator tailored for stationary/low-speed targets in distributed HAPRs, which jointly exploits signals from both active transmitters and noncooperative illuminators of opportunity (IOs). However, the joint processing of active and passive observations, coupled with the unknown signals from noncooperative IOs, leads to an intractable high-dimensional optimization problem. The expectation-maximization algorithm is employed to address the optimization problem with low complexity, iteratively suppressing DPI and updating the position estimate. To further reduce computational complexity, we use a sequential estimation method that decomposes the high-dimensional problem into a sequence of low-dimensional ones. We also derive the Cramér-Rao lower bound (CRLB) to provide an accurate performance benchmark for the proposed estimator. Extensive numerical analysis shows that the proposed direct estimator achieves estimation accuracy closer to the CRLB than estimators used in only active or passive distributed radars.