ABSTRACT This paper addresses moving interference targets (MITs) in passive radar detection of low‐altitude unmanned aerial vehicles (UAVs) by introducing a strategy that reinterprets strong scattering interference as target‐like responses. An online perception of MIT‐dominated areas is achieved through spatiotemporal statistical analysis and iterative processing of detection results. Targets within these identified areas are subsequently removed, effectively suppressing low‐altitude MITs and reducing the false‐alarm rate. A method for estimating the occurrence frequency of low‐altitude interference targets based on a first‐order autoregressive model is developed. This paper provides a detailed theoretical analysis of the weight‐update mechanism and includes a proof of the algorithm's convergence. Furthermore, a dynamic‐coefficient method and an adaptive‐coefficient method are introduced to enhance convergence performance and enable adaptive adjustments to changes in the target‐occurrence frequency. Simulation results demonstrate that the proposed algorithm converges within 100 iterations and achieves a postconvergence standard deviation of 0.0078. Field experiments show removal rates of 87.5% and 72.9% for low‐altitude interference targets, with corresponding UAV false‐removal rates of 20.26% and 4.17%, respectively.
Wang et al. (Thu,) studied this question.