History and Applications of Kalman Filter: A Review

Year : 2024 | Volume :02 | Issue : 01 | Page : 14-23
By
vector

Aiswarya Nair P.,

vector

Resmi R.,

  1. M.Tech Scholar, Department of Electrical and electronics Engineering, TKMCE, Kerala, India
  2. Assistant Professor, Department of Electrical and electronics Engineering, TKMCE, Kerala, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_122969’);});Edit Abstract & Keyword

The Kalman filter is a powerful algorithm that is used to estimate the dynamic system states with noisy measurements and uncertain behaviors. It is an optimal estimator that minimizes the average squared error between the estimated states and the true states, given the noisy data and a model of the system. The recursive algorithm is highly effective in tracking and predicting the state of complex systems over time. Kalman filters have been a very crucial part of estimation theory since their development in 1960. Originally used in missile tracking and aerospace engineering for specific purposes such as tracking and optometric estimations, they have come a long way and proved to be useful in the modern scenario in a plethora of engineering applications. Various modifications have already been brought to this recursive algorithm for state estimation, so that they can be used for a wide variety of applications in engineering such as object tracking, state of charge estimation, and localization to name a few. The main types of Kalman filters discussed in this paper are the traditional Kalman filter, the extended Kalman filter, the unscented Kalman filter, the cubature Kalman filter, and the ensemble Kalman filter. Each of these variations of the algorithm adopt unique mathematical approaches which are different from each other to solve the problem at hand. In presented paper a review of the history of Kalman filter and some of its applications is presented.

Keywords: Estimation theory, Kalman filter, extended Kalman filter, unscented Kalman filter, cubature, ensemble Kalman filter

[This article belongs to International Journal of Electrical Power and Machine Systems (ijepms)]

How to cite this article:
Aiswarya Nair P., Resmi R.. History and Applications of Kalman Filter: A Review. International Journal of Electrical Power and Machine Systems. 2024; 02(01):14-23.
How to cite this URL:
Aiswarya Nair P., Resmi R.. History and Applications of Kalman Filter: A Review. International Journal of Electrical Power and Machine Systems. 2024; 02(01):14-23. Available from: https://journals.stmjournals.com/ijepms/article=2024/view=0

Full Text PDF

References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_122969’);});Edit

  1. Maddams The scope and limitations of curve fitting.Appl Spectrosc. 1980; 34 (3): 245–267.
  1. Milanese M, Tempo Optimal algorithms theory for robust estimation and prediction. IEEE Trans Automat Control. 1985; 30 (8): 730–738.
  1. Kalman A new approach to linear filtering and prediction problems. J Basic Eng.1960; 82: 32–45.
  2. Wiener The Extrapolation, Interpolation and Smoothing of Stationary Time Series.London, UK: Wiley Publications; 1949.
  1. McGee LA, Schmidt Discovery of the Kalman filter as a practical tool for aerospace and industry. NASA Technical Memorandum 86847.Washington, DC, USA: NASA; 1985.
  1. Urrea C, Agramonte Kalman filter: historical overview and review of its use in robotics 60 years after its creation. J Sensors. 2021; 2021: 9674015.
  1. Julier SJ, Uhlmann A new extension of the Kalman filter to nonlinear systems. Proc SPIE Signal Processing, Sensor Fusion, and Target Recognition VI. 1997; 3068. doi: 10.1117/12.280797.
  1. Evenson The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynam.2003; 53 (4): 343–367.
  1. Arasaratnam I, Haykin Cubature Kalman filters. IEEE Trans Automat Control.2009; 54 (6): 1254–1269.
  1. Liu H, Hu F, Su J, Wei X, QinComparisons on Kalman-filterbased dynamic state estimation algorithms of power systems. IEEE Access. 2020; 8: 51035–51043.
  1. Howard MD, Qu An optimal Kalman-consensus filter for distributed implementation over a dynamic communication network. IEEE Access. 2021; 9: 66696–66706.
  1. Xu Z, Yang SX, Gadsden Enhanced bioinspired backstepping control for a mobile robot with unscented Kalman filter. IEEE Access. 2020; 8: 125899–125908.
  1. De Souza DA, Batista JG, Vasconcelos FJS, Dos Reis LLN, Machado GF, Costa JR, Junior JNN, Silva JLN, Rios CSN, Júnior Identification by recursive least squares with Kalman filter (RLS-KF) applied to a robotic manipulator. IEEE Access. 2021; 9: 63779–63789.
  1. Yun J, Choi Y, Lee J, Choi S, Shin State-of-charge estimation method for lithium-ion batteries using extended Kalman filter with adaptive battery parameters. IEEE Access. 2023; 11: 90901–90915.
  2. He H, Xiong R, Zhang X, Sun F, Fan State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model. IEEE Trans Vehicular Technol.2011; 60 (4): 1461–1469.
  3. Huang Z, Schneider K, NieplochaFeasibility studies of applying Kalman filter techniques to power system dynamic state estimation.In:2007 International Power Engineering Conference (IPEC 2007),Singapore, December 3–6, 2007. pp. 376–382.
  4. Xia B, Wang H, Tian Y, Wang M, Sun W, Xu State of charge estimation of lithium-ion batteries using an adaptive cubature Kalman filter. Energies.2015; 8 (6): 5916–5936.
  5. Qin X, Bi T, YangDynamic state estimator based on WAMS during power system transient process Proc CSEE.2008; 28 (7): 19–25.

Regular Issue Subscription Review Article
Volume 02
Issue 01
Received 20/05/2024
Accepted 04/06/2024
Published 20/06/2024