A Survey of Kalman Filter Algorithms and Variants in State Estimation
Current Approaches in Science and Technology Research Vol. 15,
6 August 2021
The Kalman filter is one of the most extensively used approaches for estimating system states with unknown statistics in the areas of modem control, communication applications, and signal processing.
By selecting the appropriate estimate technique, a correct and accurate state estimation of a linear or non-linear system can be enhanced. By using several mathematical techniques to linearize the nonlinear system, state estimation can be improved.Kalman filter methods are a common methodology for nonlinear systems that produce linear, unbiased, and minimum variance estimates of unknown state vectors.We attempted to bridge the gap between the Kalman filter and its variants in terms of algorithm and performance when applied to a non-linear system in this study.When only noisy observation data is provided, the techniques mentioned here have been shown to be more effective. This work can be used as theoretical basis for further studies in a number of different directions such as to achieve high computational speed for high dimensional state estimation.
- Stochastic filtering
- Bayesian filtering
- adaptive filter
- unscented transform
- digital filters
How to Cite
- Abstract Viewed: 52 times