Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter

In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF's measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC-PLKF) and the instrumental variable-based PLKF (IV-PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB-PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB-PLKF, we also propose its velocity-constrained version (VC-PLKF) to further improve the performance. Simulations show that the UB-PLKF and VC-PLKF outperform the BC-PLKF and IV-PLKF both in non-manoeuvring and manoeuvring scenarios.

Automated summary

In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) is popular for stability and low computational burden, but suffers from bias problems due to correlated measurement vector and noise; an unbiased PLKF algorithm (UB-PLKF) is proposed to address this issue, along with a velocity-constrained version (VC-PLKF) to further improve performance, outperforming other methods in both non-manoeuvring and manoeuvring scenarios according to simulations.