H-INFINITY FILTER BASED PARTICLE FILTER FOR MANEUVERING TARGET TRACKING

Abstract

In this paper, we propose a novel H-inflnity fllter based particle fllter (H1PF), which incorporates the H-inflnity fllter (H1F) algorithm into the particle fllter (PF). The basic idea of the H1PF is that new particles are sampled by the H1F algorithm. Since the H1F algorithm can fully take into account the current measurements, when the new algorithm calculates the proposed probability density distribution, the sampling particles can take advantage of the system current measurements to predict the system state. The particles distribution we obtained approaches nearer to the state posterior probability distribution and the H1PF alleviates the sample degeneracy problem which is common in the PF, especially when the maneuvers of the target tracking are large. Furthermore, the H1F algorithm can adjust gain imbalance factor by adjusting disturbance attenuation factor, from that the new algorithm can get the compromise between the accuracy and robustness and we can obtain satisfled accuracy and robustness. Some simulations and experimental results show that the proposed particle fllter performed better than the PF and the Kalman particle fllter (KPF) in tracking maneuvering target.