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

Abstract

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.

Recursive least squares estimation methods for a class of nonlinear systems based on non‐uniform sampling

“…Compared with the AM‐RLS algorithm, the AM‐HLS algorithm has higher computational efficiency. The proposed methods in this article can be extended to study the parameter identification problems of other nonlinear systems with colored noises 90‐98 and can be applied to other fields 99‐104 such as signal analysis and engineering application systems 105‐111 . In the future work, the further investigation includes the parameter estimation methods of the scalar or multivariable nonlinear systems and the convergence analysis of the proposed algorithms.…”

Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling

“…In the future work, the further investigation includes the parameter estimation methods of other linear and nonlinear systems with colored noises [61][62][63][64] and bilinear systems [65][66][67][68] and so on. The methods proposed in the article can be applied to other literatures [69][70][71][72][73][74] such as paper-making systems, information processing, transportation communication systems [75][76][77][78][79][80] and so on.…”

Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

“…With respect to prediction systems, artificial intelligence algorithms are widely used in smart city applications for classification prediction and regression prediction such as human activity classification [ 26 , 27 ], transportation [ 28 ], and air quality prediction [ 29 , 30 , 31 , 32 ]. In [ 33 ] the authors applied the ML algorithms to predict the air quality by using the data from 750 observations with 0.95 accuracy and their prediction was successful.…”