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

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

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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

Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis

摘要

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。
- 多目標(biāo)跟蹤 /
- 隨機(jī)有限集 /
- 概率假設(shè)密度 /
- 高斯混合勢化概率假設(shè)密度 /
- 脈沖多普勒雷達(dá)
Abstract: In order to take full advantage of Doppler information for Multi-Target Tracking (MTT) in the clutter environment under the framework of emerging Random Finite Sets (RFS), an MTT algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD) for pulse Doppler radar is proposed. Based on the standard GM-CPHD, the target states are updated sequentially using Doppler measurements after updating them using position measurements, then more accurate likelihood function and state estimation are obtained. Simulation results show the effectiveness of the proposed algorithm, and the introduced Doppler information can effectively suppress clutter and evidently improve tracking performance.

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參考文獻(xiàn)(16)

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