Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm

Date published

2024-04-23

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Publisher

Taylor and Francis

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Type

Article

ISSN

2164-2583

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Citation

Maton D, Economou JT, Galvão Wall D, et al., (2024) Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm. Systems Science & Control Engineering, Volume 12, Issue 1, April 2024, Article number 2343303

Abstract

In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace.

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Software Description

Software Language

Github

Keywords

Inertial measurement unit, orientation filter, dead reckoning, gain optimization, complementary filter

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Attribution 4.0 International

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Funder/s

This research is funded by the Engineering and Physical Sciences Research Council (EPSRC) iCASE Grant reference EP/S513623/1 and BAE Systems.