An Effective Multiple Model Least Squares Method in Tracking of a Maneuvering Target  

一种用于机动目标跟踪的多模型最小二乘方案

作　　者：杨位钦[1] 贾朝晖[1] 

机构地区：[1]北京理工大学自动控制系

出　　处：《Journal of Beijing Institute of Technology》1995年第1期35+29-34,共7页北京理工大学学报（英文版）

基　　金：国防科研基金

摘　　要：A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.本文提出了描述运动目标的时间原点滑动多项式数学模型，并在此模型基础上，推导出用于目标跟踪的简化最小二乘算法．为适用于机动目标跟踪，选取了一个跟踪检测信号，确定出一套多模型滤波与预报策略．本文论述的方案的突出优点是计算量比卡尔曼滤波小得多，有利于实时实现．蒙特卡罗仿真结果说明该方案是一种适用于机动目标跟踪的优选方案．

关 键 词：Kalman filters tracking/recursive least squares maneuvering target polynomial model forgetting factor 

分 类 号：TB115[理学—数学]