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一種多模型貝努利粒子濾波機動目標(biāo)跟蹤算法

楊峰,  張婉瑩

楊峰, 張婉瑩. 一種多模型貝努利粒子濾波機動目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467
引用本文: 楊峰, 張婉瑩. 一種多模型貝努利粒子濾波機動目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467
YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467
Citation: YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467

一種多模型貝努利粒子濾波機動目標(biāo)跟蹤算法

doi: 10.11999/JEIT160467
基金項目: 

國家自然科學(xué)基金(61135001, 61374159, 61374023)，西北工業(yè)大學(xué)研究生創(chuàng)意創(chuàng)新種子基金(Z2016149)

Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking

Funds: 

The National Natural Science Foundation of China (61135001, 61374159, 61374023), Seed Foundation of Innovation and Creation of Graduate Students in Northwestern Polytechnical University (Z2016149)

  • 摘要: 交互式多模型貝努利粒子濾波器(Interacting Multiple Model Bernoulli Particle Filter, IMMBPF)適用于雜波環(huán)境下的機動目標(biāo)跟蹤。但是IMMBPF將模型信息引入粒子采樣過程中會導(dǎo)致用于逼近當(dāng)前時刻真實狀態(tài)與模型的粒子數(shù)減少，而且每次遞推各模型間的粒子都要進行交互，存在計算量過大的缺點。為提升IMMBPF中單個采樣粒子對于真實目標(biāo)狀態(tài)和模型逼近的有效性，該文提出一種改進的多模型貝努利粒子濾波器(Multiple Model Bernoulli Particle Filter, MMBPF)。預(yù)先選定每一個模型的粒子數(shù)，且模型間的粒子不需要進行交互，減少了計算負荷。模型概率由模型似然函數(shù)計算得到，在不改變模型的馬爾科夫性質(zhì)的條件下避免了小概率模型的粒子退化現(xiàn)象。仿真實驗結(jié)果表明，所提出的MMBPF與IMMBPF相比，用較少的粒子數(shù)就可獲得更優(yōu)的跟蹤性能。
    Abstract: Interacting Multiple Model Bernoulli Particle Filter (IMMBPF) is suitable for maneuvering target tracking under cluttered environment. However, when model information is introduced into particle sampling process in IMMBPF, it will lead to the number decline of particles which are applied to approaching the real state and model, and the computation load is heavy because of the interacting stage of particles in the recursion. An enhanced Multiple Model Bernoulli Particle Filter (MMBPF) is proposed to improve the effectiveness of single particle to approximate the real target state and model. The number of particles of each model is given in advance, and the posterior probability of each model is updated with the associate likelihood function, which avoids particle degeneracy without distorting the Markov property. Simulation results show that the proposed MMBPF achieves better tracking performance with fewer particles than IMMBPF.
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出版歷程
  • 收稿日期:  2016-05-09
  • 修回日期:  2016-11-28
  • 刊出日期:  2017-03-19

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