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雜波環(huán)境下基于全鄰模糊聚類的聯(lián)合概率數(shù)據(jù)互聯(lián)算法

劉俊,  劉瑜,  何友,  孫順

劉俊, 劉瑜, 何友, 孫順. 雜波環(huán)境下基于全鄰模糊聚類的聯(lián)合概率數(shù)據(jù)互聯(lián)算法[J]. 電子與信息學(xué)報, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849
引用本文: 劉俊, 劉瑜, 何友, 孫順. 雜波環(huán)境下基于全鄰模糊聚類的聯(lián)合概率數(shù)據(jù)互聯(lián)算法[J]. 電子與信息學(xué)報, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849
LIU Jun, LIU Yu, HE You, SUN Shun. Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849
Citation: LIU Jun, LIU Yu, HE You, SUN Shun. Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849

雜波環(huán)境下基于全鄰模糊聚類的聯(lián)合概率數(shù)據(jù)互聯(lián)算法

doi: 10.11999/JEIT150849
  • 1.

    (海軍航空工程學(xué)院信息融合研究所 煙臺 264001) ②(北京航空航天大學(xué)電子信息工程學(xué)院 北京 100191)

基金項目: 

國家自然科學(xué)基金(61471383)

Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter

  • 1.

    (Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China)

Funds: 

The National Natural Science Foundation of China (61471383)

  • 摘要: 針對雜波環(huán)境下的多目標(biāo)跟蹤數(shù)據(jù)互聯(lián)問題，該文提出基于全鄰模糊聚類的聯(lián)合概率數(shù)據(jù)互聯(lián)算法(Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering, ANFCJPDA)。該算法根據(jù)確認(rèn)區(qū)域中量測的分布和點跡-航跡關(guān)聯(lián)規(guī)則構(gòu)造統(tǒng)計距離，以各目標(biāo)的預(yù)測位置為聚類中心，利用模糊聚類方法，計算相關(guān)波門內(nèi)候選量測與不同目標(biāo)互聯(lián)的概率，通過概率加權(quán)融合對各目標(biāo)狀態(tài)與協(xié)方差進(jìn)行更新。仿真分析表明，與經(jīng)典的聯(lián)合概率數(shù)據(jù)互聯(lián)算法(Joint Probabilistic Data Association algorithm, JPDA)相比，ANFCJPDA較大程度地改善了算法的實時性，并且跟蹤精度與JPDA相當(dāng)。
    Abstract: This paper proposes a new Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering (ANFCJPDA) for mutitarget tracking in the clutter. Firstly, distance measure is established according to measurements distribution in validation area and data correlation rules. Then, the predicted position is set up as a cluster center, and the association probabilities are calculated on the basis of fuzzy clustering, which are used as weights to update targets state and the covariance. Simulation results show that the proposed method reduces highly the computational complexity compared to conventional Joint Probabilistic Data Association (JPDA) technique, and is effective for multiple target tracking in a cluttered environment.
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出版歷程
  • 收稿日期:  2015-07-16
  • 修回日期:  2016-03-08
  • 刊出日期:  2016-06-19

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