ReviewSensors (Basel, Switzerland)2021
The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An Inverse Generalized Conversion Filter for State Estimation in Nonlinear Adversarial Sensing Systems.Sensors (Basel, Switzerland) · 2026Article
- Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models.Sensors (Basel, Switzerland) · 2024Article
- A Suboptimal Optimizing Strategy for Velocity Vector Estimation in Single-Observer Passive Localization.Sensors (Basel, Switzerland) · 2023Article
- Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter.Sensors (Basel, Switzerland) · 2022Article
- Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm.Sensors (Basel, Switzerland) · 2021Article
- Adaptive Two-Step Bearing-Only Underwater Uncooperative Target Tracking with Uncertain Underwater Disturbances.Entropy (Basel, Switzerland) · 2021Article
- Multi-Channel Fusion Classification Method Based on Time-Series Data.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
Funding
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.
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