Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach
This addresses data-target pairing for unmanned systems in applications such as search and rescue, but it is incremental as it applies existing methods to this domain.
The paper tackled the problem of multi-target localization by using machine learning algorithms like K-means clustering and SVM to learn data patterns from spatially distributed sensors, with performance quantified and compared through simulation examples.
Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovative target location learning approach, where numerous machine learning approaches, including K-means clustering and supported vector machines (SVM), are used to learn the data pattern across a list of spatially distributed sensors. To enable the accurate data association from different sensors for accurate target localization, appropriate data pre-processing is essential, which is then followed by the application of different machine learning algorithms to appropriately group data from different sensors for the accurate localization of multiple targets. Through simulation examples, the performance of these machine learning algorithms is quantified and compared.