Abstract In this paper, the problem of multi-sensor centralized state fusion with heavy-tailed process and measurement noises is considered. In order to improve the sensor fusion estimation, a novel identify-fusion strategy is proposed. An indicator, which is modelled by a Bernoulli prior, for each measurement in each sensor to identify whether the measurement is an outlier. A Student-t based hierarchical Gaussian state space model is then constructed, and the fusion is formulated as a state space estimation problem. The state and unknown parameters in the Student-t based hierarchical Gaussian state space model are jointly inferred by the variational Bayesian technique. Computer simulations are provided to demonstrate the effectiveness of the proposed method.

中文翻译：

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具有重尾噪声的稳健传感器融合

摘要 本文考虑了具有重尾过程和测量噪声的多传感器集中状态融合问题。为了改进传感器融合估计，提出了一种新的识别融合策略。一个指标，由伯努利先验建模，用于每个传感器中的每个测量值，以识别测量值是否为异常值。然后构建基于 Student-t 的分层高斯状态空间模型，并将融合公式化为状态空间估计问题。基于Student-t的分层高斯状态空间模型中的状态和未知参数通过变分贝叶斯技术联合推断。提供了计算机模拟来证明所提出方法的有效性。