A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization

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書誌詳細
出版年:	arXiv.org (May 30, 2017), p. n/a
第一著者:	Silva, Samuel
その他の著者:	Rengan Suresh, Feng, Tao, Votion, Johnathan, Cao, Yongcan
出版事項:	Cornell University Library, arXiv.org
主題:	Localization Neural networks Traffic surveillance Multilayers Error correction Clustering Traffic management Target recognition Multiple target tracking Machine learning Pattern recognition Computer simulation Search and rescue missions
オンライン･アクセス:	Citation/Abstract Full text outside of ProQuest
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その他の書誌記述
抄録:	Data-target association is an important step in multi-target localization for the intelligent operation of un- manned systems in numerous applications such as search and rescue, traffic management and surveillance. The objective of this paper is to present an innovative data association learning approach named multi-layer K-means (MLKM) based on leveraging the advantages of some existing machine learning approaches, including K-means, K-means++, and deep neural networks. To enable the accurate data association from different sensors for efficient target localization, MLKM relies on the clustering capabilities of K-means++ structured in a multi-layer framework with the error correction feature that is motivated by the backpropogation that is well-known in deep learning research. To show the effectiveness of the MLKM method, numerous simulation examples are conducted to compare its performance with K-means, K-means++, and deep neural networks.
ISSN:	2331-8422
ソース:	Engineering Database