State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unknown noise covariances coexist. To tackle this challenge, we formulate the joint estimation of system states, noise parameters, and network reliability as a Bayesian variational inference problem, and propose a novel variational Bayesian adaptive Kalman filter (VB-AKF) to approximate the joint posterior probability densities of the latent parameters. Unlike existing AKF that separately handle missing data and measurement outliers, the proposed VB-AKF adopts a dual-mask generative model with two independent Bernoulli random variables, explici...
| Authors | Peng Sun, Ruoyu Wang, Xue Luo |
|---|---|
| Primary category | stat.ML |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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