多移动传感器多目标无源定位跟踪  

Passive Location Tracking for Mobile Multi-sensors Multi-targets

作　　者：丁树宇 刘伟峰[1,2] 文成林[1] 

机构地区：[1]杭州电子科技大学自动化学院,杭州310018 [2]信息系统工程重点实验室,南京210007

出　　处：《指挥信息系统与技术》2014年第6期45-51,共7页Command Information System and Technology

基　　金：国家自然科学基金(61175030;61333011;61271144)资助项目

摘　　要：无源定位跟踪是一种非线性系统状态估计问题。为了提高系统定位精度和降低系统复杂度,将无迹卡尔曼滤波(UKF)应用于多移动传感器多目标无源定位跟踪系统。由于多移动传感器多目标交叉定位时会产生大量虚假点,随着传感器和目标数量的增加而大幅增加。因此,提出了一种改进的快速精确定位算法,即首先通过预测点选取传感器-目标测量方程;然后变换该测量方程,排除大量虚假点;再进行基于距离的支持度非等权值融合;最后将UKF子滤波估计值进行融合得到融合估计值。仿真结果表明,UKF和基于距离的融合法相结合对多移动传感器多目标无源定位具有较高的定位精度和较好的跟踪效果。Passive location tracking is a nonlinear system state estimation problem.The unscented Kalman filter（UKF）method is applied in the mobile multi-sensor multi-target passive location system to improve system location accuracy and reduce system complexity.The ghost problem exists in the mobile multi-sensor multi-target cross location.And the number of ghosts increases rapidly with the number of sensors and targets.To solve the problem,an improved fast precise location algorithm is presented.Firstly,the sensor-target function is chosen by the predicted point and the measurement function is changed for eliminating some ghosts.Then,the unequalweighted fusion is achieved based on the distance.Finally,the UKF sub-filtered estimation values are fused for obtaining a fusion estimation value.Simulation results show that mobile multisensor multi-target passive location using UKF and the fusion based on distance can precisely localize and track the target.

关 键 词：无源定位跟踪 无迹卡尔曼滤波 移动多传感器 虚假点 数据融合 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]