激光与光电子学进展, 2016, 53 (12): 122501, 网络出版: 2016-12-14
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 下载: 533次
Improvement and Application of IMM-UKF Algorithm in Two Coordinate Radar-Optoelectronic Fusion Tracking System
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 optoelectronics data fusion interacting multiple model unscented Kalman filter radar resist outliers
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。
Abstract
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 model 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.
李珂, 李醒飞, 杨帆. 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[J]. 激光与光电子学进展, 2016, 53(12): 122501. Li Ke, Li Xingfei, Yang Fan. Improvement and Application of IMM-UKF Algorithm in Two Coordinate Radar-Optoelectronic Fusion Tracking System[J]. Laser & Optoelectronics Progress, 2016, 53(12): 122501.