Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2343303 |
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| author | Dariusz Maton John T. Economou David Galvão Wall Irfan Khan Robert Cooper David Ward Simon Trythall |
| author_facet | Dariusz Maton John T. Economou David Galvão Wall Irfan Khan Robert Cooper David Ward Simon Trythall |
| author_sort | Dariusz Maton |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c3acf623bde24781a35781ec9e5eb651 |
| institution | DOAJ |
| issn | 2164-2583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-c3acf623bde24781a35781ec9e5eb6512025-08-20T02:49:30ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832024-12-0112110.1080/21642583.2024.2343303Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithmDariusz Maton0John T. Economou1David Galvão Wall2Irfan Khan3Robert Cooper4David Ward5Simon Trythall6Centre for Defence Engineering, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, UKCentre for Defence Engineering, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, UKCentre for Defence Engineering, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, UKCentre for Defence Engineering, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, UKCentre for Defence Engineering, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, UKBAE Systems, Rochester, UKBAE Systems, Rochester, UKIn 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.https://www.tandfonline.com/doi/10.1080/21642583.2024.2343303Inertial measurement unitorientation filterdead reckoninggain optimizationcomplementary filter |
| spellingShingle | Dariusz Maton John T. Economou David Galvão Wall Irfan Khan Robert Cooper David Ward Simon Trythall Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm Systems Science & Control Engineering Inertial measurement unit orientation filter dead reckoning gain optimization complementary filter |
| title | Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm |
| title_full | Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm |
| title_fullStr | Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm |
| title_full_unstemmed | Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm |
| title_short | Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm |
| title_sort | indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm |
| topic | Inertial measurement unit orientation filter dead reckoning gain optimization complementary filter |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2343303 |
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