基于IAUKF-JPDA算法的多目标跟踪  

Multi-target Tracking Based on IAUKF-JPDA Algorithm

作　　者：陈云 邹杰 武梦洁[1,2] CHEN Yun;ZOU Jie;WU Mengjie(Science and Technology on Electro-Optic Control Laboratory,Luoyang 471000,China;Luoyang Institute of Electro-Optical Equipment,AVIC,Luoyang 471000,China)

机构地区：[1]光电控制技术重点实验室,河南洛阳471000 [2]中国航空工业集团公司洛阳电光设备研究所,河南洛阳471000

出　　处：《电光与控制》2022年第4期32-36,43,共6页Electronics Optics & Control

基　　金：航空科学基金(2019ZC013004)。

摘　　要：针对不同传感器融合跟踪精度低的问题,提出一种采用加权融合的改进自适应无迹卡尔曼滤波算法(IAUKF)。针对不同传感器的融合处理中场景切换导致的传感器精度变化,对来源于不同传感器的数据设置不同的权重,引入Sage-Husa自适应思想,实时处理测量噪声的统计特性,利用联合概率数据关联(JPDA)来完成杂波的去除和量测-目标的关联任务。运用所提算法在修正球坐标系下跟踪多个空中目标,最后仿真结果表明,与基于标准UKF估计算法的对应方法相比,新算法有效减小了状态估计误差,提高了跟踪精度。To solve the problem of low tracking accuracy when fusing different sensors,an Improved Adaptive Unscented Kalman Filter(IAUKF) algorithm using weighted data fusion is proposed.In the processing of fusing different sensors,scene switching will cause decline in sensor accuracy.Through introducing the idea of Sage-Husa adaptive filtering,different weights are set for data from different sensors,and the statistical characteristics of measurement noise are processed in real time.Joint Probabilistic Data Association(JPDA) is used to remove clutter and associate measurement with target trajectories.This algorithm is used to track multiple aerial targets in the modified spherical coordinate system.The simulation results show that the new algorithm effectively reduces state estimation errors and improves tracking accuracy in comparison with the corresponding method based on the standard UKF algorithm.

关 键 词：多目标跟踪 修正球坐标系 AUKF JPDA 数据融合 

分 类 号：TP732.2[自动化与计算机技术—检测技术与自动化装置]