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The multi-sensor multi-target localization and data fusion problem is discussed, and a novel data fusion method called grid-based probability density matrix (GBPDM) is proposed. Dividing the common observe space into numerous of small grids, the measurements containing uncertainties can be represented by their sampled probability density functions. By adding probability density of all measurements...
The paper presents a multi-hop adaptive and iterative localization (MAIL) algorithm for localization wireless sensor network (WSN) nodes. The present study determines the uncertainty in the localization of nodes caused by the variation in the received signal strength or in the angle of arrival of a signal received by such nodes. An iterative localization algorithm is then proposed to decrease this...
Localization has received considerable attention because many wireless sensor network applications require accurate knowledge of the locations of the sensors in the network. The two main localization techniques are distance measurement and angle-of-arrival measurement. The former requires extensive calculations to resolve sign ambiguities in calculated angles, while the latter requires careful calibration...
Video and infrared cameras can be used in a variety of applications for measurements and recognitions. But these entire situations are affected by different degrees of uncertainty. In order to evaluate this uncertainty we propose a new method of mesurement based on fuzzy similarity. From two independent systems based on video and respectively on infrared cameras, two uncertainty evaluations are obtained...
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