Information-theoretic waypoint planning}and machine learning through Bayesian inference are exploited to track and localize a dynamically-moving radio-frequency (RF) emitter with unknown waveform (uncooperative target). The target-state estimator handles non-Gaussian distributions while mutual information is exploited to coordinate the motion control of a network of mobile sensors (agents) to minimize measurement uncertainty. The mutual information is computed for pairs of sensors through a four-permutation with replacement process. The information surfaces are fused to create a composite map, which is utilized by agents to plan their motion for more efficient and effective target estimation and tracking. Simulations and physical experiments involving micro-aerial vehicles with time difference of arrival (TDOA) measurements are performed to evaluate the performance of the algorithm. Results show that when two or three agents are used, the algorithm outperforms state-of-the-art methods. Results also show that for four or more agents, the performance is as competitive as an idealized static sensor network.

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