A single observer passive tracking algorithm based on maneuvering detection

Abstract

Single-Station passive location is a hot topic in target tracking research. A new tracking filter method was proposed for the high non-linear singer observer passive location and tracking (SOPLAT) system, where the traditional tracking filters are easy divergence and low tracking precision. The proposed method used the sequential importance resampling particle filter(SIRPF) to track the target in the initial phase, and then switch to different filter algorithms (including extended Kalman Filter (EKF) and unscented Kalman Filter (UKF)) to keep the tracking of the target according to the maneuver values. At the same time monitoring the target when the target's maneuver values are larger than the critical value return to SIRPF algorithm recapture the target. The proposed algorithm improved the precision of the tracking for the EKF and UKF and reduced the computational complexity for the SIRPF. The computer simulations reveal that this algorithm is effective.