In order to avoid the flaw of single-sensor tracking system

a multisensor multitarget tracking algorithm based on particle probability hypothesis density（PHD） filter is proposed.This algorithm can not only avoid data association problem of multisensor multitarget tracking

but also can track a time-varying number of targets robustly and their states under miss detection

dense target

cross track and large number of targets under clutter environment

achieving real-time performance.Simulation result validated multisensor with particle PHD filter performance and compared single sensor particle PHD with and multisensor with particle PHD filter performance.