GS11-6 Simulated Kalman Filter with Randomized Q and R Parameters

Title:	GS11-6 Simulated Kalman Filter with Randomized Q and R Parameters
Publication:	ICAROB2017
Volume:	22
Pages:	711-714
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2017.GS11-6
Author(s):	Nor Hidayati Abdul Aziz, Nor Azlina Ab Aziz, Zuwairie Ibrahim, Saifudin Razali, Mohd Falfazli Mat Jusof, Khairul Hamimah Abas, Mohd Saberi Mohamad, Norrima Mokhtar
Publication Date:	January 19, 2017
Keywords:	Optimization, simulated Kalman filter, random, error covariance, process noise, measurement noise
Abstract:	Inspired by Kalman filtering, simulated Kalman filter (SKF) has been introduced as a new population-based optimization algorithm. The SKF is not a parameter-less algorithm. Three parameter values should be assigned to P, Q, and R, which denotes error covariance, process noise, and measurement noise, respectively. While analysis of P has been studied, this paper emphasizes on Q and R parameters. Instead of using constant values for Q and R, random values are used in this study. Experimental result shows that the use of randomized Q and R values did not degrade the performance of SKF and hence, one step closer to the realization of a parameter-less SKF.
PDF File:	https://alife-robotics.co.jp/members2017/icarob/data/html/data/GS_pdf/GS11/GS11-6.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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