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arXiv · 1705.10757

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

Samuel Silva·Rengan Suresh·Feng Tao·Johnathan Votion·Yongcan Cao
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Abstract

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.

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Samuel Silva, Rengan Suresh, Feng Tao, Johnathan Votion, Yongcan Cao. 2017-05-30. A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization. https://arxiv.org/abs/1705.10757

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