Multi-Target Tracking with Gaussian Mixture CPHD Filter and Gating Technique

Abstract

Compared with the probability hypothesis density (PHD) filter, the cardinalized probability hypothesis density (CPHD) filter can give more accurate estimates of target number and the states of targets. The cost of increased accuracy is an increase in computational complexity. Fortunately, the computational cost of CPHD filter can be decreased by reducing the cardinality of measurement set. The gating techniques used in traditional tracking algorithms can be utilized to reduce the cardinality of measurement set. In this chapter, the elliptical gating technique is incorporated in the Gaussian mixture-CPHD filter to reduce the computational cost. Simulation results show that this method can improve the computational efficiency without losing too much estimation accuracy.