Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms
Attitude estimation methods provide modern consumer, industrial, and space systems with an estimate of a body orientation based on noisy sensor measurements. The gradient descent algorithm is one of the most recent methods for optimal attitude estimation, whose iterative nature demands adequate adju...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/2298 |
| _version_ | 1827755485843947520 |
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| author | Karla Sever Leonardo Max Golušin Josip Lončar |
| author_facet | Karla Sever Leonardo Max Golušin Josip Lončar |
| author_sort | Karla Sever |
| collection | DOAJ |
| description | Attitude estimation methods provide modern consumer, industrial, and space systems with an estimate of a body orientation based on noisy sensor measurements. The gradient descent algorithm is one of the most recent methods for optimal attitude estimation, whose iterative nature demands adequate adjustment of the algorithm parameters, which is often overlooked in the literature. Here, we present the effects of the step size, the maximum number of iterations, and the initial quaternion, as well as different propagation methods on the quality of the estimation in noiseless and noisy conditions. A novel figure of merit and termination criterion that defines the algorithm’s accuracy is proposed. Furthermore, the guidelines for selecting the optimal set of parameters in order to achieve the highest accuracy of the estimate using the fewest iterations are proposed and verified in simulations and experimentally based on the measurements acquired from an in-house developed model of a satellite attitude determination and control system. The proposed attitude estimation method based on the gradient descent algorithm and complementary filter automatically adjusts the number of iterations with the average below 0.5, reducing the demand on the processing power and energy consumption and causing it to be suitable for low-power applications. |
| first_indexed | 2024-03-11T08:10:01Z |
| format | Article |
| id | doaj.art-9009e69841c94169b712156e8aa65a87 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:01Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9009e69841c94169b712156e8aa65a872023-11-16T23:12:59ZengMDPI AGSensors1424-82202023-02-01234229810.3390/s23042298Optimization of Gradient Descent Parameters in Attitude Estimation AlgorithmsKarla Sever0Leonardo Max Golušin1Josip Lončar2Department of Communication and Space Technologies, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, CroatiaDepartment of Communication and Space Technologies, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, CroatiaDepartment of Communication and Space Technologies, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, CroatiaAttitude estimation methods provide modern consumer, industrial, and space systems with an estimate of a body orientation based on noisy sensor measurements. The gradient descent algorithm is one of the most recent methods for optimal attitude estimation, whose iterative nature demands adequate adjustment of the algorithm parameters, which is often overlooked in the literature. Here, we present the effects of the step size, the maximum number of iterations, and the initial quaternion, as well as different propagation methods on the quality of the estimation in noiseless and noisy conditions. A novel figure of merit and termination criterion that defines the algorithm’s accuracy is proposed. Furthermore, the guidelines for selecting the optimal set of parameters in order to achieve the highest accuracy of the estimate using the fewest iterations are proposed and verified in simulations and experimentally based on the measurements acquired from an in-house developed model of a satellite attitude determination and control system. The proposed attitude estimation method based on the gradient descent algorithm and complementary filter automatically adjusts the number of iterations with the average below 0.5, reducing the demand on the processing power and energy consumption and causing it to be suitable for low-power applications.https://www.mdpi.com/1424-8220/23/4/2298attitude estimationrotational quaternionEuler anglesgradient descent algorithmcomplementary filteroptimization |
| spellingShingle | Karla Sever Leonardo Max Golušin Josip Lončar Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms Sensors attitude estimation rotational quaternion Euler angles gradient descent algorithm complementary filter optimization |
| title | Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms |
| title_full | Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms |
| title_fullStr | Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms |
| title_full_unstemmed | Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms |
| title_short | Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms |
| title_sort | optimization of gradient descent parameters in attitude estimation algorithms |
| topic | attitude estimation rotational quaternion Euler angles gradient descent algorithm complementary filter optimization |
| url | https://www.mdpi.com/1424-8220/23/4/2298 |
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