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Improved K-Means Algorithm for Nearby Target Localization
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DOI：10.1109/ACCESS.2024.3479091.png)
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摘要
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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.
Keyword:
Clustering algorithms
Interference
Location awareness
Autonomous aerial vehicles
Accuracy
Prediction algorithms
Direction-of-arrival estimation
Target tracking
Trajectory
Direction of arrival
passive location
K-means clustering
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
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