Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach




Suresh, Kasthurirengan

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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 thesis is to present an innovative target location learning approach, where numerous machine learning approaches, including K-means clustering, Deep Neural network (DNN), and supported vector machines (SVM) are used to learn the data pattern across a list of spatially distributed sensors. Two new algorithms, MLKM and Extended MLKM are developed combining the structure of DNN and the unsupervised learning capability of K-means++ to solve the problem. 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.


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Data Fusion, Machine Learning, Unsupervised Learning



Electrical and Computer Engineering