NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks
AbstractThe problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs. View Full-Text
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Hua, J.; Yin, Y.; Lu, W.; Zhang, Y.; Li, F. NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks. Sensors 2018, 18, 2991.
Hua J, Yin Y, Lu W, Zhang Y, Li F. NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks. Sensors. 2018; 18(9):2991.Chicago/Turabian Style
Hua, Jingyu; Yin, Yejia; Lu, Weidang; Zhang, Yu; Li, Feng. 2018. "NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks." Sensors 18, no. 9: 2991.
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