Abstract:

Data association technology is the key part in multi-sensor target tracking systems, and is even more important under the circumstance of multitargets. Because of the measurements of multi-targets interfering each other, the lack of priori knowledge of tracking environment and restriction of sensor’s performance, the introduced error is unavoidable during the measuring process and the tracking is difficult. Aiming at solving these problems, a new algorithm based on the joint probability data association method combining with evidence theory is used to make association under a dense clutter environment. After optimization of multi-sensor information, the influence from measure errors is lowered. From the simulation result, it can be seen that the improved algorithm greatly advances tracking accurancy and has a favourable anti-jamming ability, which is suitable for dealing with engineering problems in practice.