Deriving Kalman Filter – An Easy Algorithm

Deriving Kalman Filter – An Easy Algorithm

Amaresh Das

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Faisal Alkhateeb

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University of New Orleans

Deriving Kalman Filter – An Easy Algorithm

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Abstract

The Kalman filter may be easily understood by the econometricians, and forecasters if it is cast as a problem in Bayesian inference and if along the way some well-known results in multivariate statistics are employed. The aim is to motivate the readers by providing an exposition of the key notions of the predictive tool and by laying its derivation in a few easy steps. The paper does not deal with many other ad hoc techniques used in adaptive Kalman filtering.

References

11 Cites in Article

Reference Format

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Arnaud Doucet,Simon Godsill,Christophe Andrieu (2000). On sequential Monte Carlo sampling methods for Bayesian filtering.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Amaresh Das. 2017. \u201cDeriving Kalman Filter – An Easy Algorithm\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 17 (GJSFR Volume 17 Issue F3): .

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GJSFR Volume 17 Issue F3
Pg. 1- 6

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

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Classification

GJSFR-F Classification: MSC 2010: 11Y16

Submission ReceivedApril 13, 2017
Peer Review Double Blind
Handling Editor
Accepted May 11, 2017
Published May 21, 2017

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v1.2

Issue date

May 30, 2017

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Article Matrices

Total Score: 132

Country: United States

Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision

Authors: Amaresh Das, Faisal Alkhateeb (PhD/Dr. count: 0)

View Count (all-time): 155

Total Views (Real + Logic): 3361

Total Downloads (simulated): 1663

Publish Date: 2017 05, Tue

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Total Views: 3361

Total Downloads: 1663

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