Date of Award


Degree Name

Doctor of Philosophy


Electrical and Computer Engineering

First Advisor

Dr. Liang Dong

Second Advisor

Dr. Janos L. Grantner

Third Advisor

Dr. Ala Al-Fuqaha


Classification of ground vehicles based on acoustic signals can be employed effectively in battlefield surveillance, traffic control, border monitoring, and many other applications. Classification of multiple dynamic targets based on time varying continuous signals in WSNs is a big challenge. In this project, we tackle the problem of estimation of the number and types of multiple moving ground vehicles, that are passing through a region monitored by a wireless sensor network. This work is divided into three parts, the first is the feature extraction from the vehicle sounds where various feature extraction techniques for vehicle acoustic signal are evaluated based on different criteria. In the second part, Hidden Markov Model (HMM) is utilized as a framework for classification based on multiple hypotheses 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 vehicles for each vehicle type. In the third part, a collaborative fuzzy dynamic weighted majority voting (CFDWMV) algorithm is developed to fuse all of the local decisions and make a final decision on number of vehicles for each type. The weight of each local decision is calculated by a fuzzy inference system based on the acoustic observation signal-to-noise ratio (SNR) as well as wireless communication SNR. Thus, the CFDWMV algorithm utilizes the spatial correlation between the observations of the sensor nodes. While HMM utilizes the temporal correlation and reduce the complexity of the optimal classification algorithm.

Access Setting

Dissertation-Open Access