Hidden Markov Model based classification approach for multiple dynamic vehicles in wireless sensor networks
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.
- SourceAvailable from: Tsung-Ying Sun[show abstract] [hide abstract]
ABSTRACT: A difficult blind source separation (BSS) issue dealing with an unknown and dynamic number of sources is tackled in this study. In the past, the majority of BSS algorithms familiarize themselves with situations where the numbers of sources are given, because the settings for the dimensions of the algorithm are dependent on this information. However, such an assumption could not be held in many advanced applications. Thus, this paper proposes the adaptive neural algorithm (ANA) which designs and associates several auto-adjust mechanisms to challenge these advanced BSS problems. The first implementation is the on-line estimator of source numbers improved from the cross-validation technique. The second is the adaptive structure neural network that combines feed-forward architecture and the self-organized criterion. The last is the learning rate adjustment in order to enhance efficiency of learning. The validity and performance of the proposed algorithm are demonstrated by computer simulations, and are compared to algorithms with state of the art. From the simulation results, these have been confirmed that the proposed ANA performed better separation than others in static BSS cases and is feasible for dynamic BSS cases.Expert Systems with Applications. 01/2009;
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ABSTRACT: Wireless sensor networks can be used by the mil-itary for a number of purposes such as monitoring militant activity in remote areas and force protection. Being equipped with appropriate sensors these networks can enable detection of enemy movement, identification of enemy force and analy-sis of their movement and progress. The focus of this article is on the military requirements for flexible wireless sensor networks. Based on the main networking characteristics and military use-cases, insight into specific military requirements is given in order to facilitate the reader's understanding of the operation of these networks in the near to medium term (within the next three to eight years). The article structures the evolution of military sensor networking devices by identi-fying three generations of sensors along with their capabilities. Existing developer solutions are presented and an overview of some existing tailored products for the military environment is given. The article concludes with an analysis of outstanding engineering and scientific challenges in order to achieve fully flexible, security proved, ad hoc, self-organizing and scalable military sensor networks. Keywords— wireless sensor networks, military sensor applica-tions, joint intelligence surveillance reconnaissance (JISR), mil-itary sensors, energy efficient routing, WSN generations.
- IEEE Journal on Selected Areas in Communications. 01/2005; 23:703-713.