Gaussian mixture CPHD filter with gating technique

Abstract

Cardinalized probability hypothesis density (CPHD) filter provides more accurate estimates of target number than the probability hypothesis density (PHD) filter, and hence, also of the states of targets. This additional capability comes at the price of greater computational complexity: O ( N M 3 ) , where N is the number of targets and M is the cardinality of measurement set at each time index. It is shown that the computational cost of CPHD filter can be reduced by means of reducing the cardinality of measurement set. In practice, the cardinality of measurement set can be reduced by gating techniques as done in traditional tracking algorithms. In this paper, we develop a method of reducing the computational cost of Gaussian mixture CPHD filter by incorporating the elliptical gating technique. Computer simulation results show that the computational cost is reduced and that the tracking performance loss incurred is not significant.