Simulated Kalman Filter with Randomized Q and R Parameters


Abdul Aziz, Nor Hidayati and Ab Aziz, Nor Azlina and Ibrahim, Zuwairie and Razali, Saifudin and Mat Jusof, Mohd Falfazli and Abas, Khairul Hamimah and Mohamad, Mohd Saberi and Mokhtar, Norrima (2018) Simulated Kalman Filter with Randomized Q and R Parameters. In: International Conference on Artificial Life and Robotics (ICAROB), 19-22 Jan 2017, Miyazaki, Japan.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Kalman filtering, Optimization, simulated Kalman filter, random, error covariance, process noise, measurement noise
Subjects: Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 25 Nov 2020 17:29
Last Modified: 21 Dec 2022 06:28


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