A Multitarget Tracking Algorithm Based on Radar and Infrared Sensor Data Fusion
In this article we proposed a sequential CM-PHD Filter (Gaussian Mixture Probability Hypothesis Density Filter). We extended the existing GM-PHD, which is fit for single-sensor multi-target tracking, to a multi-sensor multi-target case, and applied it in radar and infrared sensor multi-target tracking problem. Considering the infrared sensor is more accurate in angle information measuring than radar but can not get the distance information, we first estimate the observations collected by radar with the GM-PHD filter, we set the estimate results of the radar as the predicted values of the infrared sensor, then the multi-target states are updated at the fusion center with the infrared sensor observations. Simulation shows this algorithm can get more accurate estimate results than single radar multi-target tracking with GM-PHD filter, and it is robust under the clutter and omission.
GM-PHD random finite sets multitarget tracking radar/infrared sensor dula fusion
Chen Huazhi Rongjian
Institute of Physical Electronics UESTC Cheng du, China
国际会议
西安
英文
367-371
2011-05-13（万方平台首次上网日期，不代表论文的发表时间）