Use of Kalman Filter and Its Variants in State Estimation: A Review-Reference-Cited by-同舟云学术

Use of Kalman Filter and Its Variants in State Estimation: A Review

Published:2021 Issue: Volume: Page:213-230
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Container-title:Artificial Intelligence for a Sustainable Industry 4.0
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Author:

Kumari Neha,Kulkarni Rohan,Ahmed Mohammed Riyaz,Kumar Naresh

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-77070-9_13

Reference30 articles.

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2. A.J. Kanekar, A. Feliachi, State estimation using artificial neural networks, in [1990] Proceedings. The Twenty-Second Southeastern Symposium on System Theory, (IEEE Computer Society, 1990), pp. 552–553

3. J.M.C. Clark, R.B. Vinter, M.M. Yaqoob, Shiftedrayleigh filter: A new algorithm for bearings-only tracking. IEEE Trans. Aerosp. Electron. Syst. 43(4), 1373–1384 (2007)

4. T. Malkamki, S.J. Ovaska, Optimal state estimation for improved power measurements and model verification: Theory, in 2011 International Green Computing Conference and Workshops, (IEEE, 2011), pp. 1–6

5. A. Sharma, S.C. Srivastava, S. Chakrabarti, Testing and validation of power system dynamic state estimators using real time digital simulator (RTDS). IEEE Trans. Power Syst. 31(3), 2338–2347 (2015)

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