Distributed information-based clustering of heterogeneous sensor data

Heterogeneous sensing systems, consisting of sensors with different sensing capabilities, offer flexibility and provide multiple views of the sensed field by acquiring different types of measurements. The acquired sensor measurements are affected by different and unknown in number phenomena/sources of interest. To this end, a novel canonical correlation analysis (CCA) framework equipped with norm-one and norm-two regularization terms is designed to cluster the sensor data based on their information content. Block coordinate descent (BCD) is combined with the alternating direction method of multipliers (ADMM) framework to derive a centralized algorithm tackling the novel regularized CCA framework. Further, splitting of the regularized CCA into localized minimization subtasks across sensors enables distributed clustering of heterogeneous data based on their information content. Numerical tests demonstrate that the novel framework can achieve higher probability of correct clustering than existing alternatives.