Online Moving Interference Target Perception and Suppression for Passive Radar Detection of UAVs

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.