Passive Radar Tracking in Clutter Using Range and Range-Rate Measurements

Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars.

Automated summary

Passive bistatic radar research is crucial for accurate 3D target tracking, especially when facing missing or low-quality bearing information. To address the bias introduced by traditional extended Kalman filter (EKF) methods, we propose the use of unscented Kalman filter (UKF) to handle nonlinearities in 3D tracking with range and range-rate measurements. Moreover, we incorporate the probabilistic data association (PDA) algorithm with UKF to deal with cluttered environments. Through extensive simulations, we demonstrate the successful implementation of the UKF-PDA framework, which effectively reduces bias and significantly enhances tracking capabilities in passive bistatic radars.