Target Detection for Distributed Hybrid Active-Passive Radars

Hybrid active-passive radars (HAPRs) integrate the sensing advantages of both active and passive radars. Active radars offer high sensing accuracy due to known transmitted waveforms, while passive radars provide improved low probability of interception (LPI) by utilizing non-cooperative illuminators of opportunity (IOs). Although the potential of HAPRs has attracted increasing attention in recent years, testing problems and corresponding detectors for distributed HAPRs remain limited. We formulate a composite binary hypothesis testing problem by incorporating heterogeneous observations from active and passive radars. To address this problem, two detectors are developed following the generalized likelihood ratio test (GLRT) and Wald criterion, respectively. Given the potential differences in observation quality between active and passive radars, arising from differences in system parameters, channel responses, and signal processing mechanisms, traditional global fusion of all active and passive observations may lead to performance degradation. To mitigate this issue, we propose improved GLRT and Wald detectors based on a local fusion strategy that selectively fuses high-quality observations. Simulation results demonstrate that global fusion-based HAPR detectors outperform pure active or pure passive detectors under specific scenarios. Furthermore, the HAPR detectors with local fusion consistently achieve superior detection probabilities and wider coverage, verifying their robustness and effectiveness.