Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter
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摘要: 在有重尾的过程噪声和量测噪声的影响下，高斯混合势均衡多目标多伯努利滤波器(GM-CBMeMBer)的滤波性能会明显下降。针对上述问题，该文提出一种新的学生 t 混合势均衡多目标多伯努利滤波器(STM-CBMeMBer)。该滤波器将过程噪声和量测噪声近似为学生 t 分布，并用学生 t 混合模型来近似多目标的先验强度。从理论上推导出学生 t 混合形式的预测强度和后验强度，建立了势均衡多目标多伯努利滤波器的闭式递推框架。仿真结果表明，在重尾的过程噪声和量测噪声存在的环境中，该滤波器能有效抑制其干扰，相比于传统方法，具有更高的跟踪精度。
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关键词:
- 多目标跟踪 /
- 重尾噪声 /
- 势均衡多目标多伯努利 /
- 学生 t 分布 /
- 闭式递推框架
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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