Title: Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach,
Abstract: 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.