Tracking Maneuvering Target with Angle-Only Measurements Using IMM Algorithm Based on CKF

Mingjie Wan, Pengfei Li, Tao Li
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引用次数: 28

Abstract

The interacting multiple model (IMM) algorithm is specially designed to track accurately targets whose state and measurement models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. Recently, the cubature Kalman filter (CKF) has been proposed to estimate the nonlinear system, and could perform better than unscented Kalman filter (UKF). In this paper, the IMM algorithm CKF based (IMM-CKF) is presented to track the maneuvering target using angular measurements. The simulation results demonstrate the improved performance of IMM-CKF over IMM-UKF, and the execution time of the former algorithm is shorter than the latter.
基于CKF的单角度机动目标跟踪IMM算法
交互多模型(IMM)算法是专门针对运动转换过程中状态和测量模型发生变化的目标进行精确跟踪而设计的。然而,当这些模型是非线性时,为了保证精确的跟踪,必须对IMM算法进行修改。近年来,人们提出了一种比无气味卡尔曼滤波器(UKF)更好的非线性系统估计方法——常压卡尔曼滤波器(CKF)。本文提出了一种基于CKF的IMM算法(IMM-CKF),利用角度测量对机动目标进行跟踪。仿真结果表明,IMM-CKF算法的性能优于IMM-UKF算法,且前者的执行时间比后者短。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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