15. Optimization of Multi-target Tracking Within a Sensor Network Via Information Guided Clustering

This work presents a new algorithm for rapid and efficient clustering of sensing nodes within a heterogeneous wireless sensor network. The objective is to enable optimal sensor allocation for localization uncertainty reduction in multi-target tracking. The proposed algorithm is built on three metrics: (i) sensing feasibility; (ii) measurement quality to maximize information utility; and, (iii) communication cost to minimize data routing time. The derived cluster is employed as the search-space for optimal sensor allocation via maximizing the uncertainty reduction of the expected probability distribution over a target’s state-space. Theoretical analysis is used to show advantage of the proposed method in terms of information utility over the widely used Euclidean distance based clustering approach. The analysis is verified via simulated target tracking examples, in terms of metrics of information utility and computational expenditure. Simulations also reveal relationships between sensor field density and the extent of information gain over competing methods.