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.
@article{arxiv.1705.10757,
title = {A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization},
author = {Samuel Silva and Rengan Suresh and Feng Tao and Johnathan Votion and Yongcan Cao},
journal= {arXiv preprint arXiv:1705.10757},
year = {2017}
}