Joint spatial registration and multi-target tracking using an extended probability hypothesis density filter

An extended probability hypothesis density filter for translational measurement registration and multi-target tracking (MTT) is proposed. The number and states of the targets and the biases of the sensors are jointly estimated by this method without the data association. The sequential Monte Carlo method is used to implement the proposed algorithm considering non-linear and non-Gaussian conditions. Monte Carlo simulation results show that the proposed method (i) outperforms, although computationally a little more expensive than, the standard PHD filter which does not involve the process of spatial registration; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.