A Survey of Kalman Filter Algorithms and Variants in State Estimation
In the areas of modem control, communication applications, and signal processing, the Kalman filter is one of the most often used algorithms for estimating system states given unknown statistics. A correct and accurate state estimation of a linear or non-linear system can be improved by using the suitable estimate technique. To linearize the data, numerous mathematical techniques were used. The state estimation of a nonlinear system can be improved. Kalman filter methods offer linear, unbiased, and least variance estimates of unknown state vectors and are a common tool for nonlinear systems. In this study, we aimed to bridge the algorithmic and performance gap between the Kalman filter and its variants when applied to a non-linear system. When you only have When there is a lot of noisy observation data, the strategies discussed here have been proved to be more effective. This work can serve as a theoretical foundation for future research in a variety of areas, such as achieving high computing performance for high-dimensional state estimation.
Author (S) Details
Department of Electronics & Communication Engineering, University Institute of Engineering & Technology, CSJM University Kanpur, Uttar Pradesh, India.
Department of Electronics Engineering, HBTI, Kanpur, Uttar Pradesh, India.
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