High-degree cubature joint probabilistic data association information filter for multiple sensor multiple target tracking
Bin Jia, Ming Xin · 2014
In this paper, a new joint probabilistic data association information filter (JPDAIF) is proposed based on a high-degree cubature rule to improve the multiple sensor multiple target tracking performance. The cubature rule embedded JPDAIF can achieve more accurate estimation than that of joint probabilistic data association filters based on the linearization or unscented transformation. Simulation of tracking two maneuvering targets with two sensors is used to demonstrate the excellent performance of the proposed filter and compare it with several other conventional filters.