| HU Zhentao（胡振涛）*,YANG Linlin*,HU Yumei**,YANG Shibo*.[J].高技术通讯(英文),2022,28(2):181~189 |
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| Gaussian-Student’s t mixture distribution PHD robust filtering algorithm based on variational Bayesian inference |
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| DOI：10.3772/j.issn.1006-6748.2022.02.008 |
| 中文关键词: |
| 英文关键词: multi-target tracking (MTT), variational Bayesian inference, Gaussian-Student’s t mixture distribution, heavy-tailed noise |
| 基金项目: |
| Author Name | Affiliation | | HU Zhentao（胡振涛）* | (*School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China)
(**School of Automation, Northwestern Polytechnical University, Xi’an 710029, P.R.China) | | YANG Linlin* | (*School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China)
(**School of Automation, Northwestern Polytechnical University, Xi’an 710029, P.R.China) | | HU Yumei** | (*School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China)
(**School of Automation, Northwestern Polytechnical University, Xi’an 710029, P.R.China) | | YANG Shibo* | (*School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China)
(**School of Automation, Northwestern Polytechnical University, Xi’an 710029, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking (MTT) system, a new Gaussian-Student’s t mixture distribution probability hypothesis density (PHD) robust filtering algorithm based on variational Bayesian inference (GST-vbPHD) is proposed. Firstly, since it can accurately describe the heavy-tailed characteristics of noise with outliers, Gaussian-Student’s t mixture distribution is employed to model process noise and measurement noise respectively. Then Bernoulli random variable is introduced to correct the likelihood distribution of the mixture probability, leading hierarchical Gaussian distribution constructed by the Gaussian-Student’s t mixture distribution suitable to model non-stationary noise. Finally, the approximate solutions including target weights, measurement noise covariance and state estimation error covariance are obtained according to variational Bayesian inference approach. The simulation results show that, in the heavy-tailed noise environment, the proposed algorithm leads to strong improvements over the traditional PHD filter and the Student’s t distribution PHD filter. |
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