FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3137956598> ?p ?o ?g. }

• W3137956598 endingPage "2085" @default.
• W3137956598 startingPage "2085" @default.
• W3137956598 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." @default.
• W3137956598 created "2021-03-29" @default.
• W3137956598 creator A5025536472 @default.
• W3137956598 creator A5031227420 @default.
• W3137956598 creator A5049895789 @default.
• W3137956598 creator A5067818846 @default.
• W3137956598 creator A5088907463 @default.
• W3137956598 date "2021-03-16" @default.
• W3137956598 modified "2023-10-18" @default.
• W3137956598 title "The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods" @default.
• W3137956598 cites W1979731521 @default.
• W3137956598 cites W1995960411 @default.
• W3137956598 cites W2011504567 @default.
• W3137956598 cites W2028436885 @default.
• W3137956598 cites W2064675550 @default.
• W3137956598 cites W2076186907 @default.
• W3137956598 cites W2076546887 @default.
• W3137956598 cites W2105934661 @default.
• W3137956598 cites W2115307050 @default.
• W3137956598 cites W2120679422 @default.
• W3137956598 cites W2121990344 @default.
• W3137956598 cites W2126362307 @default.
• W3137956598 cites W2160337655 @default.
• W3137956598 cites W2166417161 @default.
• W3137956598 cites W2344510161 @default.
• W3137956598 cites W2405118140 @default.
• W3137956598 cites W2409156664 @default.
• W3137956598 cites W2516232829 @default.
• W3137956598 cites W2560332100 @default.
• W3137956598 cites W2576806004 @default.
• W3137956598 cites W2582188783 @default.
• W3137956598 cites W2607383521 @default.
• W3137956598 cites W2614481326 @default.
• W3137956598 cites W2624921137 @default.
• W3137956598 cites W2752533924 @default.
• W3137956598 cites W2767257026 @default.
• W3137956598 cites W2767490306 @default.
• W3137956598 cites W2771877920 @default.
• W3137956598 cites W2783513665 @default.
• W3137956598 cites W2783556645 @default.
• W3137956598 cites W2789154978 @default.
• W3137956598 cites W2791596545 @default.
• W3137956598 cites W2795713901 @default.
• W3137956598 cites W2796551753 @default.
• W3137956598 cites W2809367148 @default.
• W3137956598 cites W2839526029 @default.
• W3137956598 cites W2890369708 @default.
• W3137956598 cites W2893328278 @default.
• W3137956598 cites W2894448136 @default.
• W3137956598 cites W2896604477 @default.
• W3137956598 cites W2900191042 @default.
• W3137956598 cites W2903250535 @default.
• W3137956598 cites W2905369679 @default.
• W3137956598 cites W2906727181 @default.
• W3137956598 cites W2915945464 @default.
• W3137956598 cites W2917832368 @default.
• W3137956598 cites W2921319277 @default.
• W3137956598 cites W2923730465 @default.
• W3137956598 cites W2936908195 @default.
• W3137956598 cites W2940265767 @default.
• W3137956598 cites W2942392852 @default.
• W3137956598 cites W2948300920 @default.
• W3137956598 cites W2949754992 @default.
• W3137956598 cites W2949856406 @default.
• W3137956598 cites W2952534775 @default.
• W3137956598 cites W2958339834 @default.
• W3137956598 cites W2963384393 @default.
• W3137956598 cites W2963590654 @default.
• W3137956598 cites W2963750652 @default.
• W3137956598 cites W2964314726 @default.
• W3137956598 cites W2970682416 @default.
• W3137956598 cites W2978787066 @default.
• W3137956598 cites W2979757634 @default.
• W3137956598 cites W2981704113 @default.
• W3137956598 cites W2982824732 @default.
• W3137956598 cites W2984034812 @default.
• W3137956598 cites W2987623830 @default.
• W3137956598 cites W2989887134 @default.
• W3137956598 cites W2993509553 @default.
• W3137956598 cites W2997444524 @default.
• W3137956598 cites W2998096733 @default.
• W3137956598 cites W2998978620 @default.
• W3137956598 cites W3001278556 @default.
• W3137956598 cites W3004033697 @default.
• W3137956598 cites W3004483480 @default.
• W3137956598 cites W3005177200 @default.
• W3137956598 cites W3006048679 @default.
• W3137956598 cites W3009546766 @default.
• W3137956598 cites W3009822251 @default.
• W3137956598 cites W3010182686 @default.
• W3137956598 cites W3010421437 @default.
• W3137956598 cites W3016954429 @default.
• W3137956598 cites W3017977360 @default.
• W3137956598 cites W3027080090 @default.
• W3137956598 cites W3036353324 @default.
• W3137956598 cites W3043943213 @default.
• W3137956598 cites W3084014984 @default.
• W3137956598 cites W3084888580 @default.

• next