Mixed-state particle filters for multiaspect target tracking in image sequences

We introduce in this paper new mixed-state particle filter algorithms for direct target tracking in image sequences in a scenario where the true target template is unknown and changes randomly from frame to frame. We present two versions of the mixed-state particle filter tracker using respectively the sampling/importance resampling (SIR) technique and the alternative auxiliary particle filter (APF) method. Monte Carlo simulation results with heavily cluttered image sequences generated from real infrared airborne radar (IRAR) data show that the proposed algorithms have good performance and compare favorably to an alternative grid-based HMM filter by yielding similar steady-state root mean-square error (RMSE) at a much lower computational cost.