Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

This article reviews and investigates several basic recursive parameter identification
methods for a general stochastic system with colored noise (ie, output-error autoregressive …

For equation‐error autoregressive moving average systems, that is, Box–Jenkins systems,
this paper presents a filtered auxiliary model generalized extended stochastic gradient …

Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

Gaussian state space models have been used for decades as generative models of
sequential data. They admit an intuitive probabilistic interpretation, have a simple functional …

In this overview paper, data-driven learning model-based cooperative localization and
location data processing are considered, in line with the emerging machine learning and big …

To solve the problem of unknown noise covariance matrices inherent in the cooperative
localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is …

Remaining useful life estimation (RUL) is an essential part in prognostics and health
management. This paper addresses the problem of estimating the RUL from the observed …

Block-oriented nonlinear models are popular in nonlinear system identification because of
their advantages of being simple to understand and easy to use. Many different identification …

The Kalman filter is not suitable for the state estimation of linear systems with multistate
delays, and the extended state vector Kalman filtering algorithm results in heavy …