提出新方法同时估计状态、噪声并识别通信异常，提升传感器网络可靠性。
State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
- 用双重伯努利掩码建模丢包与数据造假，统一处理两类问题。
- 多传感器并发观测增强参数可辨识性，状态与噪声估计渐近逼近理论最优。
- 适合存在间歇性丢包和异常测量的分布式传感器系统应用。
本文研究分布式传感器网络中的状态估计问题，面临间歇性包丢失、观测数据被污染及未知噪声协方差共存的挑战。为此，将系统状态、噪声参数与网络可靠性联合估计建模为贝叶斯变分推断问题，提出一种新型变分贝叶斯自适应卡尔曼滤波器（VB-AKF），用于近似潜在参数的联合后验概率密度。与现有自适应卡尔曼滤波器分别处理缺失数据与测量异常不同，该方法采用双掩码生成模型，引入两个独立伯努利随机变量，显式刻画可观测通信损失与隐含数据真实性。此外，VB-AKF将多个并发观测整合进自适应滤波框架，显著提升统计可辨识性。大量数值实验验证了该方法的有效性与渐近最优性：随着传感器数量增加，参数识别与状态估计均渐近收敛至理论最优下界。
原文摘要 · Abstract (English)
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.
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