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25th SPIE Conference on Signal and Data Processing of Small Targets

作者： Haney, Philip J. Fiore, Paul D. BAE Syst 6 New England Execut Pk Burlington MA 01803 USA MIT Lincoln Lab Lexington MA 02420 USA

ISBN: (纸本)9780819497079

This paper presents a robust approach to the problem of localizing multiple emitters in cluttered environments using a network of low-cost sensors. A pseudo maximum likelihood (PML) function, whose peaks strongly indicate the number and locations of the true emitters, is generated from available sensor measurements. Localization is performed by first iteratively associating the individual sensor measurements to the PML peaks and then generating true maximum likelihood (ML) estimates of the emitter locations based on the associated measurements. These emitter location estimates are fed to an EKF-based, decentralized architecture for initiating, sustaining and sharing local track activity throughout the sensor network. The iterative PML approach is shown to be efficient with computational complexity that is linear in the number of measurements. Additionally, simulation results illustrate the ability of the proposed approach to generate accurate emitter location estimates in cluttered environments for generating and sustaining all track activity in a fully decentralized sensor network.

关键词： Emitter Localization multimodal sensor networks Maximum Likelihood Decentralized Data Fusion

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Clustering based fuzzy logic for multimodal sensor networks: A preprocessing to decision fusion

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2010年第3期2卷 271-286页

作者： Ramadan, Rabie A. Cairo Univ Fac Engn Dept Comp Engn Giza 12211 Egypt

The advances of Micro-electromechanical systems (MEMS) technology lead to new types of sensors named "multimodal" sensors where multiple features can be sensed and reported by one sensor. Forming a wireless sensor network of such sensors poses new challenges to the wireless sensor networks in addition to the current challenges. Currently, each multimodal sensor reports periodically a message for each feature or a long message that contains all the features compared to the traditional sensors. Such multimodal sensor networks could be used for multiple purposes and serve different applications. However, data handling and information processing as well as data/decision tasks became much harder than before. In this paper, we introduce a set of clustering algorithms taking into consideration the reported multiple features as well as some of the sensors parameters such as nodes' residual energy and clusterheads' degree. The paper utilizes different clustering techniques including fuzzy logic. The proposed algorithms are designed to simplify the next step operation which is data/decision fusion and decision making operations. Through an extensive set of experiments, the proposed algorithms are evaluated.

关键词： sensor networks multimodal sensor networks clustering intelligent classroom

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