Tracking for the maneuvering target based on multiple model and moving horizon estimation

Zhiqiang Jiao, Weihua Li, Qian Zhang, Peng Wang
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引用次数: 2

Abstract

This paper considers the tracking problem for the maneuvering target with the motion subject to some known physical constrains. For the target tracking problem, the moving horizon estimation (MHE) approach is firstly introduced, by which we can treat the physical constraints on the target motion as some useful knowledge. Under the MHE framework, we then adopt the multiple model (MM) method to describe different behaviours for the maneuvering target. To incorporate the MM method into MHE framework, we derive an estimation evolution formula and modify the weighting matrix update formula with considering the multiple model, then the physical constraint can be directly handled based on the evolved estimation. Based on the above procedures, the MM-MHE optimization and algorithm are finally presented for the tracking problem. Comparing with the adaptive Kalman filter (AKF) and the interacting multiple model (IMM) approaches, a better tracking performance can be achieved by applying our algorithm (especially for the physically constrained motion condition), which is demonstrated by a simple simulation example.
基于多模型和运动视界估计的机动目标跟踪
本文研究了具有已知物理约束的机动目标的跟踪问题。针对目标跟踪问题,首先引入运动视界估计(MHE)方法,该方法可以将目标运动的物理约束视为一些有用的知识。在MHE框架下,采用多模型(MM)方法描述机动目标的不同行为。为了将MM方法整合到MHE框架中,我们推导了一个估计演化公式,并在考虑多模型的情况下修改了加权矩阵更新公式,从而可以根据演化后的估计直接处理物理约束。在此基础上,最后提出了跟踪问题的MM-MHE优化算法。与自适应卡尔曼滤波(AKF)和交互多模型(IMM)方法相比,该算法具有更好的跟踪性能(特别是在物理约束运动条件下),并通过一个简单的仿真实例进行了验证。
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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