  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.
