Measurement-Driven Iterated Correction GM-PHD Filter for Passive Tracking With Hardware-in-the-Loop Simulation

Considering the characteristics of Multi Aircraft Attack and Defense (MAVAD), the problem of multisensor multitarget passive tracking is researched in this article. Firstly, to solve the problem posed by incomplete or unknown knowledge about the intensity of the target’s newborns, a practical measurement–driven method of adaptive generating newborn intensity function is adopted. Then, a method named measurement–driven iterated correction Gaussian–mixture probability hypothesis density (MD-IC-GM-PHD) filter is proposed to overcome the difficulties of the observability of targets in passive tracking. Based on the above methods, a rational and efficient approach to trajectory management is presented to complete the task of recording the historical information about target states. Finally, a hardware-in-the-loop (HIL) simulation system is implemented to verify the validity of the designed multiple target tracking (MTT) algorithms.