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Bayesian Transfer Filtering Using Pseudo Marginal Measurement Likelihood
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DOI：10.1109/TCYB.2024.3490580.png)
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摘要
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Integrating the advantage of the unbiased finite impulse response (UFIR) filter into the Kalman filter (KF) is a practical yet challenging issue, where how to effectively borrow knowledge across domains is a core issue. Existing methods often fall short in addressing performance degradation arising from noise uncertainties. In this article, we delve into a Bayesian transfer filter (BTF) that seamlessly integrates the UFIR filter into the KF through a knowledge-constrained mechanism. Specifically, the pseudo marginal measurement likelihood of the UFIR filter is reused as a constraint to refine the Bayesian posterior distribution in the KF. To optimize this process, we exploit the Kullback-Leibler (KL) divergence to measure and reduce discrepancies between the proposal and target distributions. This approach overcomes the limitations of traditional weight-based fusion methods and eliminates the need for error covariance. Additionally, a necessary condition based on mean square error criteria is established to prevent negative transfer. Using a moving target tracking example and a quadruple water tank experiment, we demonstrate that the proposed BTF offers superior robustness against noise uncertainties compared to existing methods.
Keyword:
Bayes methods
Noise
Uncertainty
Robustness
Kalman filters
Iterative methods
Estimation error
Water
Target tracking
Storage management
Bayesian transfer filtering
Kalman filter (KF)
negative transfer
pseudo marginal measurement likelihood
unbiased finite impulse response (UFIR) filter
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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引用论文
Spatial-Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning多传感器数据融合的时空联合迁移学习协同定位.png)
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