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|Title:||TDOA-Based Source Collaborative Localization via Semidefinite Relaxation in Sensor Networks||Authors:||Yan, Yongsheng
|Issue Date:||2015||Source:||Yan, Y., Wang, H., Shen, X., He, K., & Zhong, X. (2015). TDOA-Based Source Collaborative Localization via Semidefinite Relaxation in Sensor Networks. International Journal of Distributed Sensor Networks, 2015, 248970-.||Series/Report no.:||International Journal of Distributed Sensor Networks||Abstract:||The time delay of arrival- (TDOA-) based source localization using a wireless sensor network has been considered in this paper. The maximum likelihood estimate (MLE) is formulated by taking the correlated TDOA noise into account, which is caused by the difference with the TOA of the reference sensor. The global optimal solution is difficult to obtain due to the nonconvex nature of the ML function. We propose an alternative semidefinite programming method, which transforms the original ML problem into a convex one by relaxing nonconvex equalities into convex matrix inequalities. In addition, the source localization algorithm in the presence of sensor location errors and non-line-of-sight (NLOS) observations is developed. Our simulation results demonstrate the potential advantages of the proposed method. Furthermore, the proposed source localization algorithm by taking the NLOS TOA measurements as the constraints of the convex problem can provide a good estimate.||URI:||https://hdl.handle.net/10356/81076
|ISSN:||1550-1329||DOI:||http://dx.doi.org/10.1155/2015/248970||Rights:||© 2015 Yongsheng Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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