Supervisory-Review Mechanism Against Cooperative Track Spoofing in Netted Radar Systems

Cooperative track spoofing (CTS) poses an unsolved conundrum to netted radar systems (NRS) by creating increasingly persistent and precise phantom tracks. Existing anti-deception methods in NRS struggle to counter this sophisticated joint range-angle deception. To address this pressing challenge, we propose a supervisory-review mechanism with its parameter optimization to identify CTS while minimizing its impact on tracking and searching tasks of NRS. The mechanism begins with a supervisory process that involves selecting the supervisory frame and radar node, along with scheduling resources during dwell time to check targets. Following this, binary hypothesis testing in the review process determines whether to repeat the supervisory process. These steps thus enable NRS with supervisory-review mechanism to detect CTS. To enhance effectiveness, the parameter optimization incorporates a hybrid policy for selecting supervisory radar nodes and employs reinforcement learning (RL)-based resource scheduling via a constructed Markov decision process (MDP). Simulations in complex multi-target scenarios validate the effectiveness of the proposed mechanism in recognizing CTS and demonstrate the advantages of RL-based resource scheduling.