Micro-Doppler-Assisted Particle Filtering for Tracking of Doppler-Blind-Zone Targets With Passive Radar

Abstract

Robust detection and tracking of unmanned aerial vehicles (UAVs) in passive radar systems remains challenging when targets enter the Doppler-blind-zone (DBZ), where severe energy attenuation and ground clutter contamination cause model mismatch and degrade tracking performance. To address this issue, this study proposes a micro-Doppler-assisted particle filtering track-before-detect (mD-PF-TBD) algorithm. Firstly, within a bistatic geometry based on digital terrestrial multimedia broadcast (DTMB) signals, a detailed mD motion model of multi-rotor UAVs is established, and an observation model incorporating mD signatures is derived. To mitigate the model mismatch-induced particle weight degradation, a variable-particle strategy is introduced, which adaptively redistributes particles according to variations in Doppler frequency. As the target Doppler frequency approaches zero, the algorithm allocates more particles to the mD harmonic side peaks, which remain detectable outside the DBZ, thereby maintaining robust tracking. The proposed method is validated through both simulations and field experiments. Monte Carlo simulations demonstrate that the variable-particle strategy significantly enhances tracking performance for DBZ targets. Field experiments using DJI M300 RTK and M600 UAVs confirm that the proposed algorithm maintains stable tracking and accurate state estimation, even when the target remains in the DBZ for an extended duration.