Multiple Target Track Based on RBPF and Data Association

【Abstract】Particle filter approximates to the posterior probability density function with a set of weighted random sample points and realizes accurate estimation of arbitrary state model. It combines Rao-Blackwellized Particle Filter(RBPF) with Multiple Hypothesis Tracking(MHT) and separates multiple target track problem into two parts: estimation of the posterior probability distribution of data association and estimation of the single target track based on the data association. The former can be solved by Sequential Importance Resampling(SIR), and the latter can be solved by minimum mean square error estimation with Kalman filter. Experimental results show that the calculation particle count and the calculation amount can be reduced by using optimal importance distribution.