Target locating algorithm based on curve-fitting in wireless sensor networks

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Focusing on the accuracy of target locating in wireless sensor networks, a target locating algorithm based on curve-fitting is proposed. With least square fitting to the positions of all targets, the curve parameters of the approximate target traveling path are calculated. Moreover, more localization information is introduced to participate in target locating via piecewise curve fitting, thus erroneous factor is reduced in the process of localization. Finally, the localization and tracking of the target is realized. Test results indicate that the proposed algorithm brings higher target locating precision than general curve fitting method, thus the accuracy of target locating in wireless sensor networks can be improved effectively.

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The sparse target location algorithm based on orth can solve the problem that the sampling dictionary does not satisfy the RIP property. Compared with the traditional method, the orth preprocessing can reduce the energy consumption and communication overhead, but the orth pretreatment will affect the sparsity of the original signal. So that the positioning accuracy is affected to a certain extent. In this paper, a sparse target location algorithm based on QR-decomposition is proposed. On the basis of orth algorithm, the sampling dictionary is decomposed by QR, which can’t change the sparsity of the original signal under the premise of satisfying the RIP property. The problem of sparse target location based on network is transformed into the problem of target location based on compressed perception, and the localization error is reduced. The experimental results show that the location performance of sparse target location algorithm based on QR-decomposition and centroid algorithm is much better than that the sparse target location algorithm based on orth, and the accuracy of target location is greatly improved.
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