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In this paper, we analyze the performance of group based detection in sparse sensor networks, when the system level detection decision is made based on the detection reports generated from multiple sensing periods. Sparse deployment is essential for reducing cost of large scale sensor networks, which cover thousands of square miles. In a sparse deployment, the sensor field is only partially covered...
This correspondence describes an efficient Bayesian framework for localization of moving terminals (MTs) in wideband wireless networks. In a previous paper, the authors have presented a grid-based technique, based on a hidden Markov model, that used the power delay profiles of the received signals to track the MT position. This grid-based Bayesian method has proved its efficacy in reducing localization...
This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances from three sensors inside a triangle. This point is indeed at the trianglepsilas center of gravity. We solve the problems of large errors and poor performance in the bounding box algorithm...
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