一種幅度信息輔助多伯努利濾波算法

袁常順; 王俊; 孫進(jìn)平; 孫忠勝; 畢嚴(yán)先

doi:10.11999/JEIT150683

一種幅度信息輔助多伯努利濾波算法

doi: 10.11999/JEIT150683

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61171122, 61201318, 61471019, 61501011)，中央高校基本科研業(yè)務(wù)費(fèi)專項(xiàng)資金(YWF-15-GJSYS- 068)

計(jì)量
- 文章訪問數(shù): 1472
- HTML全文瀏覽量: 120
- PDF下載量: 480
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-06-08
- 修回日期: 2015-11-11
- 刊出日期: 2016-02-19

A Multi-Bernoulli Filtering Algorithm Using Amplitude Information

Funds:

The National Natural Science Foundation of China (61171122, 61201318, 61471019, 61501011), The Fundamental Research Funds for the Central Universities (YWF- 15-GJSYS-068)

摘要

摘要: 在許多多目標(biāo)跟蹤場(chǎng)景中，目標(biāo)返回的幅度通常強(qiáng)于虛警雜波返回的幅度。通過建立更加準(zhǔn)確的包含幅度信息的目標(biāo)和虛警雜波似然函數(shù)，可提高多目標(biāo)估計(jì)精度。該文提出一種基于隨機(jī)有限集的幅度信息輔助多伯努利濾波(Amplitude Information Assistant Multi-Bernoulli Filter, AIA-MBerF)算法。該算法通過建立幅度似然函數(shù)將幅度信息引入到多伯努利濾波的更新過程中，并給出針對(duì)線性和非線性模型的高斯混合(Gaussian Mixture, GM)和序貫蒙特卡洛(Sequential Monte Carlo, SMC)實(shí)現(xiàn)方法。仿真結(jié)果表明，該濾波算法相比于傳統(tǒng)多伯努利濾波(Multi-Bernoulli Filter, MBerF)無論GM還是SMC實(shí)現(xiàn)都可獲得更加準(zhǔn)確穩(wěn)定的目標(biāo)數(shù)和對(duì)應(yīng)的目標(biāo)狀態(tài)估計(jì)。
- 多目標(biāo)跟蹤 /
- 隨機(jī)有限集 /
- 幅度信息 /
- 多伯努利濾波
Abstract: In many multi-target tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This amplitude information can be used to improve the multi-target state estimation by obtaining more accurate target and false-alarm likelihoods. In this paper, a novel multi-Bernoulli filtering algorithm is proposed, which is based on the random finite set and incorporate the amplitude information. The amplitude likelihood functions are derived to incorporate the amplitude information into the multi-Bernoulli filter in the update step. In addition, a Gaussian Mixture (GM) implementation for the linear model and a Sequential Monte Carlo (SMC) implementation for the non-linear model are proposed. Simulation results for Gaussian Mixture and Sequential Monte Carlo implementations show that the proposed filter demonstrates a significant improvement than conventional multi-Bernoulli filter in the estimation accuracy of both the number of targets and their states.
- Multi-target tracking /
- Random finite set /
- Amplitude information /
- Multi-Bernoulli filter

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參考文獻(xiàn)(24)

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