Improved K-Means Algorithm for Nearby Target Localization
In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources,...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10714343/ |
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| author | Zongwen Yuan Xingdi Wang Fuyang Chen Xicheng Ma |
| author_facet | Zongwen Yuan Xingdi Wang Fuyang Chen Xicheng Ma |
| author_sort | Zongwen Yuan |
| collection | DOAJ |
| description | In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources, the accuracy of traditional localization algorithms based on direction finding and cross-fixing deteriorates. In this paper, we propose a localization algorithm based on K-means clustering. To tackle the problem of unknown initial positions of target sources, we employ a grid density peak clustering(DPC) method for initial localization. In the K-means algorithm, we integrate a quartile range anomaly detection algorithm to address interference signal issues. Finally, we propose an invalid compensation algorithm to filter out invalid signals, thereby compensating for the estimation errors in angles. Through the collection of real-world data, we compare the performance of the traditional direction finding and cross-fixing algorithms with the proposed algorithm in the localization of nearby target points. Experimental results demonstrate that the proposed algorithm significantly improves localization accuracy. |
| format | Article |
| id | doaj-art-11d155841c7443a1bd5cd0c3973c2b87 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-11d155841c7443a1bd5cd0c3973c2b872025-01-28T00:01:16ZengIEEEIEEE Access2169-35362025-01-0113148721488010.1109/ACCESS.2024.347909110714343Improved K-Means Algorithm for Nearby Target LocalizationZongwen Yuan0Xingdi Wang1https://orcid.org/0009-0007-3272-5566Fuyang Chen2https://orcid.org/0000-0003-2650-420XXicheng Ma3School of Electronic Engineering, Chaohu University, Hefei, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaSchool of Electronic Engineering, Chaohu University, Hefei, ChinaBeijing Measurement and Control Technology Company Ltd., Beijing, ChinaIn a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources, the accuracy of traditional localization algorithms based on direction finding and cross-fixing deteriorates. In this paper, we propose a localization algorithm based on K-means clustering. To tackle the problem of unknown initial positions of target sources, we employ a grid density peak clustering(DPC) method for initial localization. In the K-means algorithm, we integrate a quartile range anomaly detection algorithm to address interference signal issues. Finally, we propose an invalid compensation algorithm to filter out invalid signals, thereby compensating for the estimation errors in angles. Through the collection of real-world data, we compare the performance of the traditional direction finding and cross-fixing algorithms with the proposed algorithm in the localization of nearby target points. Experimental results demonstrate that the proposed algorithm significantly improves localization accuracy.https://ieeexplore.ieee.org/document/10714343/Direction of arrivalpassive locationK-means clustering |
| spellingShingle | Zongwen Yuan Xingdi Wang Fuyang Chen Xicheng Ma Improved K-Means Algorithm for Nearby Target Localization IEEE Access Direction of arrival passive location K-means clustering |
| title | Improved K-Means Algorithm for Nearby Target Localization |
| title_full | Improved K-Means Algorithm for Nearby Target Localization |
| title_fullStr | Improved K-Means Algorithm for Nearby Target Localization |
| title_full_unstemmed | Improved K-Means Algorithm for Nearby Target Localization |
| title_short | Improved K-Means Algorithm for Nearby Target Localization |
| title_sort | improved k means algorithm for nearby target localization |
| topic | Direction of arrival passive location K-means clustering |
| url | https://ieeexplore.ieee.org/document/10714343/ |
| work_keys_str_mv | AT zongwenyuan improvedkmeansalgorithmfornearbytargetlocalization AT xingdiwang improvedkmeansalgorithmfornearbytargetlocalization AT fuyangchen improvedkmeansalgorithmfornearbytargetlocalization AT xichengma improvedkmeansalgorithmfornearbytargetlocalization |