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基于高斯混合勢化概率假設(shè)密度的脈沖多普勒雷達(dá)多目標(biāo)跟蹤算法

吳衛(wèi)華 江晶 馮訊 劉重陽

吳衛(wèi)華, 江晶, 馮訊, 劉重陽. 基于高斯混合勢化概率假設(shè)密度的脈沖多普勒雷達(dá)多目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報(bào), 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232
引用本文: 吳衛(wèi)華, 江晶, 馮訊, 劉重陽. 基于高斯混合勢化概率假設(shè)密度的脈沖多普勒雷達(dá)多目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報(bào), 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232
Wu Wei-hua, Jiang Jing, Feng Xun, Liu Chong-yang. Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232
Citation: Wu Wei-hua, Jiang Jing, Feng Xun, Liu Chong-yang. Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232

基于高斯混合勢化概率假設(shè)密度的脈沖多普勒雷達(dá)多目標(biāo)跟蹤算法

doi: 10.11999/JEIT141232
基金項(xiàng)目: 

國家自然科學(xué)基金(61102168)資助課題

Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis

  • 摘要: 為在新興的隨機(jī)有限集(RFS)框架下充分利用多普勒信息跟蹤雜波環(huán)境下的多目標(biāo),該文提出基于高斯混合勢化概率假設(shè)密度(GM-CPHD)的脈沖多普勒雷達(dá)多目標(biāo)跟蹤(MTT)算法。該算法在標(biāo)準(zhǔn)GM-CPHD基礎(chǔ)上,在使用位置量測更新狀態(tài)后,再利用多普勒量測進(jìn)行序貫更新,可獲得更精確的似然函數(shù)和狀態(tài)估計(jì)。仿真結(jié)果驗(yàn)證了該算法的有效性,表明在GM-CPHD基礎(chǔ)上引入目標(biāo)的多普勒信息可有效抑制雜波,顯著改善跟蹤性能。
  • Mahler R. Statistical Multisource-multitarget Information Fusion[M]. Norwood: Artech House, 2007.
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    Beard M, Vo B T, Vo B N, et al.. A partially uniform target birth model for Gaussian mixture PHD/CPHD filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2835-2844.
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    Ristic B, Vo B-N, Clark D, et al.. A metric for performance evaluation of multi-target tracking algorithms[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3452-3457.
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出版歷程
  • 收稿日期:  2014-09-23
  • 修回日期:  2014-12-15
  • 刊出日期:  2015-06-19

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