Bearings-Only Target Tracking Based on Hybrid Filter

In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) algorithm has attracted much attention due to its concise calculation process and robust operation performance. However, due to the correlation between the measurement matrix and the noise, the algorithm often encounters the challenge of estimation bias when dealing with the target state estimation. Aiming at this problem, this paper skillfully combines the accuracy of particle filter (PF) and the efficiency of PLKF, and innovatively proposes an algorithm called particle filter-pseudolinear Kalman filter (PF-PLKF). The algorithm first performs pseudo-linear transformation on the measurement equation, then separates the noise part of the measurement matrix, and then uses the PF algorithm to accurately estimate the denoised measurement matrix, which greatly reduces the error caused by the correlation between the measurement matrix and the noise. The simulation results show that the PF-PLKF algorithm performs well in the bearing-only target tracking task. Compared with other existing algorithms, it achieves a significant improvement in tracking accuracy and maintains a high standard in stability.