13 April 2009 Soft adaptive fusion of sensor energy for large-scale sensor networks (SAFE)
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Abstract
Target tracking for network surveillance systems has gained significant interest especially in sensitive areas such as homeland security, battlefield intelligence, and facility surveillance. Most of the current sensor network protocols do not address the need for multi-sensor fusion-based target tracking schemes, which is crucial for the longevity of the sensor network. In this paper, we present an efficient fusion model for target tracking in a cluster-based large sensor networks. This new scheme is inspired by the image processing techniques by perceiving a sensor network as an energy map of sensor stimuli and applying typical image processing techniques on this map such as: filtering, convolution, clustering, segmentation, etc to achieve high-level perceptions and understanding of the situation. The new fusion model is called Soft Adaptive Fusion of Sensor Energies (SAFE). SAFE performs soft fusion of the energies collected by a local region of sensors in a large-scale sensor network. This local fusion is then transmitted by the head node to a base-station to update the common operation picture with evolving events of interest. Simulated scenarios showed that SAFE is promising by demonstrating a significant improvement in target tracking reliability, uncertainty, and efficiency.
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Haroun Rababaah, Amir Shirkhodaie, "Soft adaptive fusion of sensor energy for large-scale sensor networks (SAFE)", Proc. SPIE 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009, 734506 (13 April 2009); doi: 10.1117/12.818944; https://doi.org/10.1117/12.818944
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