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基于自適應(yīng)的增廣狀態(tài)-交互式多模型的機(jī)動(dòng)目標(biāo)跟蹤算法

許紅,  謝文沖,  袁華東,  段克清,  王永良

許紅, 謝文沖, 袁華東, 段克清, 王永良. 基于自適應(yīng)的增廣狀態(tài)-交互式多模型的機(jī)動(dòng)目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報(bào), 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516
引用本文: 許紅, 謝文沖, 袁華東, 段克清, 王永良. 基于自適應(yīng)的增廣狀態(tài)-交互式多模型的機(jī)動(dòng)目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報(bào), 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516
Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG. Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516
Citation: Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG. Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516

基于自適應(yīng)的增廣狀態(tài)-交互式多模型的機(jī)動(dòng)目標(biāo)跟蹤算法

doi: 10.11999/JEIT190516 cstr: 32379.14.JEIT190516
  • 1.

    海軍工程大學(xué)電子工程學(xué)院 武漢 430033

  • 2.

    空軍預(yù)警學(xué)院 武漢 430019

  • 3.

    中山大學(xué)電子與通信工程學(xué)院 廣州 510006

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61871397)
詳細(xì)信息
    作者簡(jiǎn)介:

    許紅：男，1991年生，博士生，研究方向?yàn)槔走_(dá)數(shù)據(jù)處理

    謝文沖：男，1978年生，副教授，主要研究方向?yàn)闄C(jī)載雷達(dá)信號(hào)處理、空時(shí)自適應(yīng)信號(hào)處理等

    袁華東：男，1985年生，博士生，研究方向?yàn)槔走_(dá)數(shù)據(jù)處理、陣列信號(hào)處理

    段克清：男，1981年生，副教授，主要研究方向?yàn)榭諘r(shí)自適應(yīng)信號(hào)處理、陣列信號(hào)處理等

    王永良：男，1965年生，教授，主要研究方向?yàn)槔走_(dá)信號(hào)處理、空時(shí)自適應(yīng)信號(hào)處理等

    通訊作者:

    許紅　xuhongzhxu@163.com

  • 中圖分類號(hào): TN957.51; TP391.41

Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model

  • 1.

    College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

  • 2.

    Air Force Early Warning Academy, Wuhan 430019, China

  • 3.

    School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China (61871397)
  • 摘要: 現(xiàn)有的增廣狀態(tài)-交互式多模型算法存在著依賴于量測(cè)噪聲協(xié)方差矩陣這一先驗(yàn)信息的問題。當(dāng)先驗(yàn)信息未知或不準(zhǔn)確時(shí)，算法的跟蹤性能將會(huì)下降。針對(duì)上述問題，該文提出一種自適應(yīng)的變分貝葉斯增廣狀態(tài)-交互式多模型算法VB-AS-IMM。首先，針對(duì)增廣狀態(tài)的跳變馬爾科夫系統(tǒng)，該文給出了聯(lián)合估計(jì)增廣狀態(tài)和量測(cè)噪聲協(xié)方差矩陣的變分貝葉斯推斷概率模型。其次，通過理論推導(dǎo)證明了該概率模型是非共軛的。最后，通過引入一種“信息反饋+后處理”方案，提出聯(lián)合后驗(yàn)密度的次優(yōu)求解方法。所提算法能夠在線估計(jì)未知的量測(cè)噪聲協(xié)方差矩陣，具有更強(qiáng)的魯棒性和適應(yīng)性。仿真結(jié)果驗(yàn)證了算法的有效性。
    Abstract: The existing Augmented State-Interracting Multiple Model (AS-IMM) algorithm suffers from the problem that it relies on the prior information of the covariance matrix of the measurement noise. When the prior information is unavailable or inaccurate, the tracking performance of AS-IMM will be degraded. In order to overcome this problem, a novel adaptive Bayesian Variational Augmented State-Interracting Multiple Model (VB-AS-IMM) algorithm is proposed. Firstly, the variational Bayesian inference probabilistic model of the augmented state and the covariance matrix of the measurement noise for the jump Markovarian system is presented. Secondly, the probabilistic model is proven to be non-conjugated. Finally, by introducing a novel post processing method, the suboptimal solution to calculate the joint posterior distribution is proposed. The proposed algorithm can estimate the unknown covariance matrix of the measurement noise online, thus it is more robust and has higher adaptability. Simulation result verifies good performance of the proposed algorithm.
    • HTML全文

  • 圖  1  仿真場(chǎng)景

    圖  2  量測(cè)協(xié)方差矩陣先驗(yàn)信息不準(zhǔn)確時(shí)算法跟蹤性能

    圖  3  各算法的模型概率

    表  1  算法的平均RMSE

    算法位置RMSE(m)速度RMSE(m/s)
    MIMM131.692.37
    IMM191.913.37
    MAS-IMM78.900.95
    AS-IMM90.941.47
    VB-AS-IMM79.740.96
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2019-07-10
  • 修回日期:  2020-02-28
  • 網(wǎng)絡(luò)出版日期:  2020-09-01
  • 刊出日期:  2020-11-16

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