Signal Detection Theory-Based Localization Method in Urban NLOS Environment

Abstract

Location based service (LBS) plays an important role in smart city system. However, there is serious non-line of sight (NLOS) phenomenon in high-density urban areas, which affects the localization accuracy significantly. Based on signal detection theory, we propose a two-step localization method to identify NLOS signals and estimate position after mitigating the influence of NLOS. Firstly, depending on the prior probabilities, the NLOS signals are identified by generalized likelihood ratio (GLR) test or Neyman-Pearson (NP) criterion. Moreover, the NLOS signals are mitigated based on identified measurement condition. Finally, selecting residual weighting algorithm (S-RWGH) is used to estimate the target position. Simulation results show that the proposed algorithm can effectively improve the localization accuracy. Average location error is below 15 m when the NLOS rate is below 62.5 % in the urban environment.