Hidden Markov Model based classification approach for multiple dynamic vehicles in wireless sensor networks

It is challenging to classify multiple dynamic targets in wireless sensor networks based on the time-varying and continuous signals. In this paper, multiple ground vehicles passing through a region are observed by audio sensor arrays and efficiently classified. Hidden Markov Model (HMM) is utilized as a framework for classification based on multiple hypothesis testing with maximum likelihood approach. The states in the HMM represent various combinations of vehicles of different types. With a sequence of observations, Viterbi algorithm is used at each sensor node to estimate the most likely sequence of states. This enables efficient local estimation of the number of source targets (vehicles). Then, each sensor node sends the state sequence to a manager node, where a collaborative algorithm fuses the estimates and makes a hard decision on vehicle number and types. The HMM is employed to effectively model the multiple-vehicle classification problem, and simulation results show that the approach can decrease classification error rate.