• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 31 Issue 1
Jan.  2018
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Article Contents
DENG Ping, RUI Yang, DENG Shuifa. Novel Scattering Path Recognition Algorithm Based on LPMD[J]. Journal of Southwest Jiaotong University, 2018, 53(1): 182-188. doi: 10.3969/j.issn.0258-2724.2018.01.022
Citation: DENG Ping, RUI Yang, DENG Shuifa. Novel Scattering Path Recognition Algorithm Based on LPMD[J]. Journal of Southwest Jiaotong University, 2018, 53(1): 182-188. doi: 10.3969/j.issn.0258-2724.2018.01.022

Novel Scattering Path Recognition Algorithm Based on LPMD

doi: 10.3969/j.issn.0258-2724.2018.01.022
  • Received Date: 27 Jan 2016
  • Publish Date: 25 Feb 2018
  • Non-line-of-sight (NLOS) error is the major source of error in ground wireless localization. In order to recognize single-bound scattering path and multiple-bound scattering paths from all the NLOS scattering paths, we propose a novel recognition algorithm based on line-of-possible-mobile-device (LPMD). This algorithm first utilizes the cross points of all the LPMDs to calculate the coordinate of a reference point. The algorithm then uses part of the cross points far away from the reference point to obtain minus expectation compensation to revise the location of reference point, builds the judgement formula of scatter path type based on the distance between each LPMD and the reference point to make single/multiple bound path judgement. Under normal and special scenery, the simulation tests of scatter path judgement are performed under different parameter conditions; the simulation results show when the distance between the target and the detecting station reaches 4 km. A false alarm probability and a false dismissal probability of the proposed algorithm can be limited to 3%and 9%, respectively. Further, these probabilities can be decreased by about 21% and 8%, respectively, when compared with the original LPMD algorithm..

     

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