Research on Radar/Infrared Fusion Algorithm Based on IMM/MS-MDPDAF

Abstract

In the clutter environment, radar/infrared sensors are used to track the background of maneuvering targets. Aiming at the shortcomings of traditional probabilistic data association theory in solving multiple target echoes and numbers in measurement, it is presented by combining interactive multiple model (IMM) and multiple detection probabilistic data association filter (MDPDAF) in a multi-sensor (radar and infrared) scenario known as IMM/MS-MDPDAF algorithm. The IMM algorithm has the ability to adapt to the target high maneuver and clutter environment. The MS-MDPDAF algorithm can detect multiple effective target echoes, considering various uncertainties in the clutter environment. According to the multi-detection mode of radar sensor, the target is effectively measured and the state vector is updated. Then the data fusion and probability data association theory are used to calculate the corresponding probability and state prediction, estimation and update under the Bayesian framework. The simulation results show that the IMM/MS-MDPDAF algorithm can improve the effectiveness and tracking accuracy of target, and has better tracking performance than IMM/MSPDAF.