How Important the Error Covariance in Simulated Kalman Filter?

How Important the Error Covariance in Simulated Kalman Filter?

The process of searching good parameter values is a non-trivial task for metaheuristic algorithms. When two algorithms are comparable in terms of speed and probability of convergence, the algorithm with less number of parameters is always preferred. This paper discussed the importance of the initial...

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Main Authors: Nor Hidayati, Abd Aziz, Zuwairie, Ibrahim, Saifudin, Razali, Bakare, Taofiq Adeola, Nor Azlina, Ab. Aziz
Format: Conference or Workshop Item
Language:English
Published: 2016
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/14582/
http://umpir.ump.edu.my/id/eprint/14582/7/P044%20pg315-320.pdf
id ump-14582
recordtype eprints
spelling ump-145822018-02-08T02:49:10Z http://umpir.ump.edu.my/id/eprint/14582/ How Important the Error Covariance in Simulated Kalman Filter? Nor Hidayati, Abd Aziz Zuwairie, Ibrahim Saifudin, Razali Bakare, Taofiq Adeola Nor Azlina, Ab. Aziz TK Electrical engineering. Electronics Nuclear engineering The process of searching good parameter values is a non-trivial task for metaheuristic algorithms. When two algorithms are comparable in terms of speed and probability of convergence, the algorithm with less number of parameters is always preferred. This paper discussed the importance of the initial error covariance parameter, P(0), in Simulated Kalman Filter (SKF) with an intent to make SKF a parameter-less algorithm. To evaluate the importance of initial error covariance value in SKF, several values were selected and statistical analyses using nonparametric Friedman and Wilcoxon signed rank tests were carried out to see if different initial error covariance has any significant difference in the final outcome. The results prove that no matter what the initial error covariance is, SKF algorithm still managed to converge to near-optimal value without any significant degradation or improvement. 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14582/7/P044%20pg315-320.pdf Nor Hidayati, Abd Aziz and Zuwairie, Ibrahim and Saifudin, Razali and Bakare, Taofiq Adeola and Nor Azlina, Ab. Aziz (2016) How Important the Error Covariance in Simulated Kalman Filter? In: Proceedings of The National Conference for Postgraduate Research (NCON-PGR 2016), 24-25 September 2016 , Universiti Malaysia Pahang (UMP), Pekan, Pahang. pp. 315-320..
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Nor Hidayati, Abd Aziz
Zuwairie, Ibrahim
Saifudin, Razali
Bakare, Taofiq Adeola
Nor Azlina, Ab. Aziz
How Important the Error Covariance in Simulated Kalman Filter?
description The process of searching good parameter values is a non-trivial task for metaheuristic algorithms. When two algorithms are comparable in terms of speed and probability of convergence, the algorithm with less number of parameters is always preferred. This paper discussed the importance of the initial error covariance parameter, P(0), in Simulated Kalman Filter (SKF) with an intent to make SKF a parameter-less algorithm. To evaluate the importance of initial error covariance value in SKF, several values were selected and statistical analyses using nonparametric Friedman and Wilcoxon signed rank tests were carried out to see if different initial error covariance has any significant difference in the final outcome. The results prove that no matter what the initial error covariance is, SKF algorithm still managed to converge to near-optimal value without any significant degradation or improvement.
format Conference or Workshop Item
author Nor Hidayati, Abd Aziz
Zuwairie, Ibrahim
Saifudin, Razali
Bakare, Taofiq Adeola
Nor Azlina, Ab. Aziz
author_facet Nor Hidayati, Abd Aziz
Zuwairie, Ibrahim
Saifudin, Razali
Bakare, Taofiq Adeola
Nor Azlina, Ab. Aziz
author_sort Nor Hidayati, Abd Aziz
title How Important the Error Covariance in Simulated Kalman Filter?
title_short How Important the Error Covariance in Simulated Kalman Filter?
title_full How Important the Error Covariance in Simulated Kalman Filter?
title_fullStr How Important the Error Covariance in Simulated Kalman Filter?
title_full_unstemmed How Important the Error Covariance in Simulated Kalman Filter?
title_sort how important the error covariance in simulated kalman filter?
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/14582/
http://umpir.ump.edu.my/id/eprint/14582/7/P044%20pg315-320.pdf
first_indexed 2023-09-18T22:18:30Z
last_indexed 2023-09-18T22:18:30Z
_version_ 1777415498668965888

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