State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
This addresses state estimation challenges in distributed sensor networks, offering an incremental improvement by integrating multiple observations and dual-mask modeling for better reliability.
The paper tackles state estimation in distributed sensor networks with intermittent packet dropouts, corrupted observations, and unknown noise covariances by proposing a variational Bayesian adaptive Kalman filter (VB-AKF), which asymptotically converges to the theoretical optimal lower bound for parameter identification and state estimation as sensor count increases.
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, explicitly characterizing both observable communication losses and latent data authenticity. Additionally, the VB-AKF integrates multiple concurrent multiple observations into the adaptive filtering framework, which significantly enhances statistical identifiability. Comprehensive numerical experiments verify the effectiveness and asymptotic optimality of the proposed method, showing that both parameter identification and state estimation asymptotically converge to the theoretical optimal lower bound with the increase in the number of sensors.