Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture

Separable Newton Recursive Estimation Method Through System Responses Based on Dynamically Discrete Measurements with Increasing Data Length

Summary This article considers the parameter estimation for a fractional‐order nonlinear finite impulse response system with colored noise. For the fractional‐order systems, the challenge and diffi...

Iterative parameter and order identification for fractional-order nonlinear finite impulse response systems using the key term separation

Summary The trajectory tracking control problem for a class of nonlinear systems with uncertain parameters is considered in this article. A new adaptive finite‐time tracking control is designed bas...

Finite‐time adaptive control for nonlinear systems with uncertain parameters based on the command filters

The data filtering based multiple-stage Levenberg–Marquardt algorithm for Hammerstein nonlinear systems

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

Ruben Jonhson Robert Jeremiah

Papers

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods