Paper Info
Title
Genetic Algorithm Based Kalman Filter Adaptation Algorithm for Magnetic and Inertial Measurement Unit
Abstract
A magnetic and inertial measurement unit (MIMU) usually measures acceleration, rotation rate, and earth's magnetic field in order to determine a body's attitude. In order to find the orientation information using all sensor information a fusion algorithm is used. Extended Kalman filtering is a well known technique that has been widely applied in many applications used for state estimation. The main idea behind the algorithm is that a series of observations over time is used to produce estimates of unknown states leading to more precise orientation information than compared to when estimated states were only based on single observations. However, one problem exists, namely the Extended Kalman filtering solution becomes very poor when abrupt acceleration motions occur. In order to avoid this problem, an optimization algorithm can be integrated into the filtering mechanism as a dynamic model correction. Thus, this paper introduces a genetic algorithm to be utilized as a noise-adaptive mechanism in order to tune the Extended Kalman filter process.
Year
DOI
Venue
2018
10.1109/CEC.2018.8477940
2018 IEEE Congress on Evolutionary Computation (CEC)
Keywords
Field
DocType
Earth magnetic field,extended Kalman filtering,noise-adaptive mechanism,optimization algorithm,abrupt acceleration motions,precise orientation information,state estimation,fusion algorithm,sensor information,magnetic measurement unit,inertial measurement unit,Kalman filter adaptation algorithm,genetic algorithm
Magnetic field,Extended Kalman filter,Computer science,Algorithm,Filter (signal processing),Kalman filter,Inertial measurement unit,Optimization algorithm,Acceleration,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
978-1-5090-6018-4
0
0.34
References 
Authors
1
1
Name
Order
Citations
PageRank
Simone A Ludwig11309179.41