Abstract
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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Acknowledgment
This work is supported by the National Natural Science Foundation of China (61771154) and the Fundamental Research Funds for the Central Universities (HEUCFG201830).
Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.
We gratefully thank of very useful discussions of reviewers.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, Z., Tao, H., Lin, Y. (2019). A Target Localization Algorithm for Wireless Sensor Network Based on Compressed Sensing. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_54
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DOI: https://doi.org/10.1007/978-3-030-19086-6_54
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