A target tracking method based on high-resolution radar photoelectronic image data fusion

Abstract

A target tracking model and a technique for target tracking filtering based on sequential unscented Kalman filter are presented to improve target tracking performance of high resolution radar/infrared imaging sensor composite guidance system. Firstly, a measurement model for imaging sensor based of the centroid of the target is derived from images. Secondly, a measurement model for radar based of the centroid of the target is derived from traits of high resolution radar. Finally, the data fusion filtering framework for target tracking based on sequential unscented Kalman filter is presented. From the results of simulated experiments, average rate and target tracking accuracy of convergence for the technique developed are superior to those of other techniques. In conclusion, the target tracking model and filtering algorithm developed are proper for high resolution radar/infrared imagery sensor composite guidance system. Key words: data fusion；target tracking；unscented conversion；Kalman filtering 1 IntroductionAt present, radar / IR imaging compound guidance has become one of the main directions for the terminal guidance technology development of precision-guided weapons. Having integrated the performance of the target tracking system, it has greatly improved upon target tracking performance of the single guided mode. Target tracking is a process of estimating the status of the target by combing the measurements by radar and infrared imaging sensors. Literature [1] carried out the Kalman filtering for each sensor, and a further Kalman filtering of the filtered results in the fusion center (KF/ KF); literature [2] implemented Kalman filtering for each sensor, and the extended Kalman filtering for the results in the fusion center (KF / EKF); literature [3] used unscented Kalman filter (UKF) to solve the nonlinear filtering problem; all these methods are limited to the situation when the measured noise is subject to Gaussian distribution. Literature [4,5] solved the temporal change problem of the parameters in the nonlinear system by building a multi-mode interaction model (IMM). Literature [6] adopted the multi-resolution filtering (MRF) method to deal with the image measurements, making them fit in the target tracking filtering framework. Literature [7,8] made use of the particle filtering (PF) to handle the filtering of the nonlinear system, and performed well for arbitrarily distributed noise. Literature [9] constructed a data integration framework using artificial intelligence (AI), and achieved satisfying results.In these tracking systems, measurement models were constructed with the target viewed as a target point. In essence, these measurement models were based on the position measurement by radar axis and that by infrared optical axis. Literature [10] considered the problems with infrared imaging measurement model; however, its radar measurement model was constructed for the ordinary radar, and did not apply to the high-resolution radar.This paper reported a study focusing on the high resolution radar / IR im...