A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization
This addresses data association for multi-target localization in unmanned systems, but it appears incremental as it builds on existing machine learning techniques.
The paper tackles the problem of data-target association in multi-target localization by proposing a multi-layer K-means (MLKM) approach, which combines K-means++ with a multi-layer framework and error correction inspired by deep learning, and shows effectiveness through simulation comparisons with existing methods.
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.