學(xué)生 t 混合勢(shì)均衡多目標(biāo)多伯努利濾波器
doi: 10.11999/JEIT181121
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1.
空軍工程大學(xué)信息與導(dǎo)航學(xué)院 西安 710077
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2.
空軍研究院 北京 100096
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3.
93658部隊(duì) 北京 100144
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61703420, 61673392)
Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter
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Institute of Information and Navigation, Aire Force Engineering University, Xi’an 710077, China
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2.
Air Force Research Institute, Beijing 100096, China
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3.
Unit 93658, Beijing 100144, China
Funds: The National Natural Science Foundation of China (61703420, 61673392)
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摘要: 在有重尾的過(guò)程噪聲和量測(cè)噪聲的影響下，高斯混合勢(shì)均衡多目標(biāo)多伯努利濾波器(GM-CBMeMBer)的濾波性能會(huì)明顯下降。針對(duì)上述問(wèn)題，該文提出一種新的學(xué)生 t 混合勢(shì)均衡多目標(biāo)多伯努利濾波器(STM-CBMeMBer)。該濾波器將過(guò)程噪聲和量測(cè)噪聲近似為學(xué)生 t 分布，并用學(xué)生 t 混合模型來(lái)近似多目標(biāo)的先驗(yàn)強(qiáng)度。從理論上推導(dǎo)出學(xué)生 t 混合形式的預(yù)測(cè)強(qiáng)度和后驗(yàn)強(qiáng)度，建立了勢(shì)均衡多目標(biāo)多伯努利濾波器的閉式遞推框架。仿真結(jié)果表明，在重尾的過(guò)程噪聲和量測(cè)噪聲存在的環(huán)境中，該濾波器能有效抑制其干擾，相比于傳統(tǒng)方法，具有更高的跟蹤精度。
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關(guān)鍵詞:
- 多目標(biāo)跟蹤 /
- 重尾噪聲 /
- 勢(shì)均衡多目標(biāo)多伯努利 /
- 學(xué)生 t 分布 /
- 閉式遞推框架
Abstract: The filtering performance of Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods. -
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