Hidden Markov Model Based Classification Approach for Multiple Dynamic Vehicles in Wireless Sensor Networks Ahmad Aljaafreh, Student Member, IEEE and Liang Dong, Senior Member, IEEE 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. I. I NTRODUCTION W IRELESS Sensor Network (WSN) is, by definition, a network of sensor nodes that are spread across a geographical area, where each sensor node has a restricted computation capability, memory, wireless communication, and power supply. In general, the objective of WSNs is to monitor, control, or track objects, processes, or events [1]. Fig 3 shows a one cluster of WSN. In WSNs, observed data could be processed at the sensor node itself; distributed over the network; or at the gateway node. Most often, nodes are battery-powered which makes power the most significant constraint in WSNs . The power consumed as a result of the typical data processing tasks executed at the sensor nodes is less than the power consumed for inter-sensor com- munication. This motivates researches and practitioners to consider decentralized data processing algorithms more than the centralized ones. Multiple-target classification in Multiple moving target classification is a real challenge [2] because of the dynamicity and mobility of targets. The dynamicity of the targets refers to the evolution of the number of targets over time. Furthermore, limited observations, power, computa- tional and communication constraints within and between the sensor nodes make it a more challenging problem. Multiple target classification can be modeled as a Blind Source Sepa- ration (BSS) problem [3]. Independent Component Analysis This work was supported in part by the DENSO North America Founda- tion and by the Faculty Research and Creative Activities Award of Western Michigan University. A. Aljaafreh and L. Dong are with the Department of Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI 49008 USA (e-mail: ahmad.f.aljaafreh@wmich.edu, liang.dong@wmich.edu). (ICA) can be utilized for such a problem. Most of the recent literature assumes a given number of sources; thus, making the aforementioned challenge easier to solve. Unfortunately, this assumption is unrealistic in many applications of wireless sensor networks. Some recent publications decouple the problem into two sub-problems, namely: the model order estimation problem and the blind source separation problem. Ref.[4] discusses the problem of source estimation in sensor network for multiple target detection. In the literature, many researchers utilized ICA for source separation while others utilized statistical methods as in [5] where the authors pre- sented a particle filtering based approach for multiple vehicle acoustic signals separation in wireless sensor networks. The previously mentioned techniques are based on data fusion. In these techniques, each sensor node detects the targets, extracts the features and sends the data to the manager node. The manager node is responsible for source separation, number estimation, and classification of the sources. The computation and communication overhead induced by such a centralized approachs inadvertently limits the lifetime of the sensor network. Classification of multiple targets without signals or sources separation based on multiple hypothesis testing is an efficient way of classification [6]. Ref. [7] proposed a distributed classifiers based on modeling each target as a zero mean stationary Gaussian random process and so the mixture sig- nals. A multi hypothesis test based on maximum likelihood is the base of the classifier. In this paper, we are proposing an algorithm to classify multiple dynamic targets based on HMM. HMM decreases the number of hypothesis that is needed to be tested at every classification query. Which decreases the computation overhead. On the other hand, emerging hypothesis transition probability with hypothesis likelihood increases the classification precision. The remain- der of this paper is organized as follows. Section 2 formulate the problem mathematically. Section 3 describes modeling the problem as HMM. Simulation environment is described in Section 4. Section 5 presents the results and discussions. And finally conclusions are described in section 6. II. PROBLEM FORMULATION Multiple ground vehicles as multiple targets are to be classified in a particular cluster region of a WSN. In this paper, any vehicle that enters the cluster region is assumed to be sensed by all the sensor nodes within this cluster. Each sensor node estimates the number and types of vehicles currently present in the region and the final decision is made 540 978-1-4244-6453-1/10/$26.00 ©2010 IEEE Authorized licensed use limited to: WESTERN MICHIGAN UNIVERSITY. Downloaded on June 08,2010 at 15:33:41 UTC from IEEE Xplore. Restrictions apply.