IMM-UKF算法在两坐标雷达-光电融合跟踪系统中的改进与应用  

Improvement and Application of IMM-UKF Algorithm in Two Coordinate Radar-Optoelectronic Fusion Tracking System

作　　者：李珂[1] 李醒飞[1] 杨帆[2] 

机构地区：[1]天津大学精密测试技术与仪器国家重点实验室,天津300072 [2]山东科技大学机械电子工程学院,山东青岛266590

出　　处：《激光与光电子学进展》2016年第12期244-253,共10页Laser & Optoelectronics Progress

基　　金：精密测试技术及仪器国家重点实验室开放基金(PIL1407);基于激光泵浦的钾光泵磁力仪关键技术研究(61503283)

摘　　要：光电与雷达的数据融合能够实现两个独立传感器测量信息的互补,改善对目标的识别跟踪能力。针对联合传感器系统对动态运动目标定位中存在野值的现象,同时为了解决单一传感器滤波跟踪发散的问题,提出一种具有抗野值性能的交互式多模型无迹卡尔曼滤波(IMM-UKF)融合算法。在两坐标雷达提供目标距离与方位角的前提下,建立参数求解模型,得到目标的俯仰角,结合光电传感器提供的角度信息进行滤波融合。实验与仿真结果表明:该算法可以有效融合雷达与光电的测量数据,排除野值的干扰,抑制滤波发散,提高定位精度。Optoelectronic and radar data fusion can achieve complementary of two single sensors measuring information, and improve target recognition and tracking capability. Aiming at the presence of outliers in localization of combined sensor system to dynamic motion target, meanwhile, in order to solve a single sensor tracking filtering divergence problem, a resist outliers interacting multiple model unscented Kalman filter （IMM- UKF） fusion algorithm is proposed. Under the condition of two coordinate radar providing target range and azimuth angle, parameter solving mode/is established and the pitch angle of target is obtained. Fusion filter is carried on by combining angle information provided by photoelectric sensor. Experiment and simulation results show that the algorithm can effectively integrate radar and optoelectronic measurement data, eliminate the interference of outliers, suppress filter divergence and improve positioning accuracy.

关 键 词：光电子学 数据融合 交互式多模型无迹卡尔曼滤波算法 雷达 抗野值 

分 类 号：TN953[电子电信—信号与信息处理] TH745[电子电信—信息与通信工程]