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Modular Interacting Multiple Model Based on Extended Viterbi Algorithm for Maneuvering Target Tracking

机译:基于扩展维特比算法的模块化交互多目标机动目标跟踪

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摘要

A new maneuvering target tracking algorithm is investigated, which is modeled as a class of Markov jump linear systems (MJLS). Drawing on the experience of combination idea of the extended Viterbi algorithm (EV) and the interacting multiple model algorithm (IMM), a modular interacting multiple model based on extended Viterbi (MIMMEV) is presented. The MIMMEV algorithm consists of N independent interacting multiple model-extended Viterbi (IMM-EV). Furthermore, these IMM-EV filters are independent and working in parallel in the MIMMEV algorithm. According to the derived probability, the estimated state of every moment is the weighted sum of each estimator at the corresponding time. Simulation results demonstrate that the proposed algorithm improves the tracking precision and reduces the computational burden compared with traditional IMM and IMM-EV.
机译:研究了一种新的机动目标跟踪算法,将其建模为一类马尔可夫跳跃线性系统(MJLS)。借鉴扩展维特比算法(EV)和交互多模型算法(IMM)结合思想,提出了一种基于扩展维特比(MIMMEV)的模块化交互多模型。 MIMMEV算法由N个独立的相互作用的多个模型扩展的维特比(IMM-EV)组成。此外,这些IMM-EV滤波器是独立的,并且在MIMMEV算法中并行工作。根据得出的概率,每个时刻的估计状态是相应时刻每个估计量的加权和。仿真结果表明,与传统的IMM和IMM-EV相比,该算法提高了跟踪精度,减少了计算量。

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  • 来源
    《Mathematical Problems in Engineering 》 |2015年第20期| 374054.1-374054.6| 共6页
  • 作者单位

    Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China;

    Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China;

    Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China;

    Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China;

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