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Bayesian hidden Markov models for UAV-enabled target localization on road networks with soft-hard data

This work addresses the problem of localizing a mobile intruder on a road network with a small UAV through fusion of event-based `hard data' collected from a network of unattended ground sensors (UGS) and `soft data' provided by human dismount operators (HDOs) whose statistical characteristics may be unknown. Current approaches to road network intruder detection/tracking have two key limitations: predictions become computationally expensive with highly uncertain target motions and sparse data, and they cannot easily accommodate fusion with uncertain sensor models. This work shows that these issues can be addressed in a practical and theoretically sound way using hidden Markov models (HMMs) within a comprehensive Bayesian framework. A formal procedure is derived for automatically generating sparse Markov chain approximations for target state dynamics based on standard motion assumptions. This leads to efficient online implementation via fast sparse matrix operations for non-Gaussian localization aboard small UAV platforms, and also leads to useful statistical insights about stochastic target dynamics that could be exploited by autonomous UAV guidance and control laws. The computational efficiency of the HMM can be leveraged in Rao-Blackwellized sampling schemes to address the problem of simultaneously fusing and characterizing uncertain HDO soft sensor data via hierarchical Bayesian estimation. Simulation results are provided to demonstrate the proposed approach.