杂波环境下多被动传感器单目标跟踪算法  

An Algorithm for Single Target Tracking with Multiple Passive Sensors in Clutter

作　　者：李彬彬[1] 冯新喜[1] 王朝英[1] 雷雨[1] 

机构地区：[1]空军工程大学电讯工程学院,西安710077

出　　处：《电光与控制》2012年第8期11-14,共4页Electronics Optics & Control

基　　金：陕西省自然科学基础研究计划项目(2011JM8023)

摘　　要：由于被动传感器只能获得目标的角度量测,因此杂波环境下基于被动传感器的关联问题较主动传感器更为困难。针对杂波环境下纯方位多被动传感器系统的单目标跟踪问题,提出了一种基于扩展卡尔曼滤波的模糊综合贴近度关联跟踪方法。该方法采用直角坐标系下多被动传感器系统的扩展卡尔曼滤波对目标进行跟踪。首先利用目标航迹的预测信息,针对每个传感器建立确认跟踪门;在获得候选关联组合后,直接利用角度信息建立各候选关联组合与角度预测值间的模糊综合贴近度,通过在所获得的全部模糊综合贴近度中寻求最优解完成量测到航迹的关联。仿真实验表明,该方法可以有效地解决杂波环境下多被动传感器系统的单目标跟踪问题。Passive sensors can only obtain the angle information from target, thus the problem of data association for passive sensors in clutter is more complex than that for active sensors. To the problem of single target tracking with the system of bearings-only multi-sensor in clutter, a new target tracking algorithm with fuzzy synthetic clingy-degree based on Extended Kalman Filter （EKF） is presented. The method employs the EKF of multiple passive sensors in Cartesian coordinate system for target tracking. Firstly, based on the predicted information of target tracking, the validation gate for each sensor is established. After the candidate association group is achieved, the fuzzy synthetic clingy-degree between each candidate association group and predicted angle can be established with the angled information, and then the optimal solution is obtained from all of fuzzy synthetic clingy-degree to solve the problem of association from measurement to track. Simulation results show that this method can effectively solve the problem of single target tracking with the system of bearings-only multi-sensor in clutter.

关 键 词：目标跟踪 多被动传感器 扩展卡尔曼滤波 确认跟踪门 模糊综合贴近度 

分 类 号：V271.4[航空宇航科学与技术—飞行器设计] TN953[电子电信—信号与信息处理]