Optimal Sensor Decision Based on Particle Filter

Introduction Multi-sensor tracking technique has receivedmuch attention in recent years. Many tracking sys-tems involve basically active (radar) and passive(infrared tracking) subsystems. The main disad-vantage of infrared search and tracking is the prob-lem of observability and filtering divergence. Actu-ally, quite a lot of research activities have beendone in this field and some researchers more thanonce simulated the feasibility of the approach1,2].The main idea of the methods is as foll…

Optimal Sensor Decision Based on Particle Filter

A novel infrared and radar synergistic tracking algorithm, which is based on the idea of closed loop control, and target's motion model identification and particle filter approach, was put forward. In order to improve the observability and filtering divergence of infrared search and tracking, the unscented Kalman filter algorithm that has stronger ability of non-linear approximation was adopted. The polynomial and least square method based on radar and IRST measurements to identify the parameters of the model was proposed, and a "pseudo sensor" was suggested to estimate the target position according to the identified model even if the radar is turned off. At last,the average Kullback-Leibler discrimination distance based on particle filter was used to measure the tracking performance, based on tracking performance and fuzzy stochastic decision, the idea of closed loop was used to retrieve the module parameter of "pseudo sensor". The experimental result indicates that the algorithm can not only limit the radar activity effectively but also keep the tracking accuracy of active/passive system well.