World Academic Union
In this paper, the authors propose a Hierarchical Hidden Markov Model (HHMM) to model the detection of M vehicles in a Wireless Sensor Network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications. This paper integrates several techniques to optimize the detection performance.