Conference Proceeding

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

Dept. of Electr. & Comput. Eng., Western Michigan Univ., Kalamazoo, MI, USA
05/2010; DOI:10.1109/ICNSC.2010.5461602 In proceeding of: Networking, Sensing and Control (ICNSC), 2010 International Conference on
Source: IEEE Xplore

ABSTRACT 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.

0 0
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: Classification of ground vehicles based on acoustic signals using wireless sensor networks is a crucial task in many applications such as battlefield surveillance, border monitoring, and traffic control. Different signal processing algorithms and techniques that are used in classification of ground moving vehicles in wireless sensor networks are surveyed in this paper. Feature extraction techniques and classifiers are discussed for single and multiple vehicles based on acoustic signals. This paper divides the corresponding literature into three main areas: feature extraction, classification techniques, and collaboration and information fusion techniques. The open research issues in these areas are also pointed out in this paper. This paper evaluates five different classifiers using two different feature extraction methods. The first one is based on the spectrum analysis and the other one is based on wavelet packet transform.
    Signal Processing : An International Journal. 01/2010;

Full-text (2 Sources)

Available from
Mar 25, 2014