当前动态系统呈现大型化, 复杂化的趋势, 基于贝叶斯滤波的动态系统状态估计遇到了一系列新
的挑战. 随着深度学习在特征提取与模式识别等方面的优势与潜力不断显现 …

Bayesian filtering serves as the mainstream framework of state estimation in dynamic
systems. Its standard version utilizes total probability rule and Bayes' law alternatively …

The Kalman filter (KF) is a popular state estimation technique that is utilized in a variety of
applications, including positioning and navigation, sensor networks, battery management …

State estimation is widely used in various automated systems, including IoT systems,
unmanned systems, robots, etc. In traditional state estimation, measurement data are …

The optimality of Bayesian filtering relies on the completeness of prior models, while deep
learning holds a distinct advantage in learning models from offline data. Nevertheless, the …

In this study, we introduce a novel residual-based Bayesian expectation–maximization
adaptive Kalman filter (RBEMAKF) for dynamic state estimation with inaccurate and time …

This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for
system identification. Although DNNs show impressive approximation ability in various …

State estimation of dynamical systems in real-time is a fundamental task in signal
processing. For systems that are well-represented by a fully known linear Gaussian state …

Kalman filter (KF) is highly valued in engineering for its simplicity, small storage, and real-
time processing. However, KF is optimal for linear filters and not as effective for nonlinear …

State estimation of dynamical systems is crucial for providing new decision-making and
system automation information in different applications. However, the assumptions on the …