Bearings-only tracking with particle filtering for joint parameter learning and state estimation

This paper considers the problem of bearings only tracking of manoeuvring targets. A learning particle filtering algorithm is proposed which can estimate both the unknown target states and unknown model parameters. The algorithm performance is validated and tested over a challenging scenario with abrupt manoeuvres. A comparison of the proposed algorithm with the Interacting Multiple Model (IMM) filter is presented. The learning particle filter has shown accurate estimation results and improved accuracy compared with the IMM filter.