Abstract:

To overcome the drawback of high computational burden and poor real-time capability in target tracking problems using particle filter with a large number of particles, an improved real-time particle filter (RTPF) algorithm is proposed which is based on data fusion with support vector machines (SVM). The SVMRTPF employs the estimation window RTPF as basic framework and uses the SVM for fusing the particles at different time in the window, so the target is tracked quickly by the particles and their updated importance weights according to the fused results. Compared with the RTPF algorithm based on minimizing the Kullback-Leibler distance to adjust mixing weights in the window, the new algorithm is simple and more suitable to the range of real-time applications. The bearings-only tracking simulation results demonstrate the feasibility and superiority of the novel algorithm.