• RSSI indoor localization through a Bayesian strategy

      Zhou, Fu; Lin, Kaixian; Ren, Aifeng; Cao, Dongjian; Zhang, Zhiya; Ur-Rehman, Masood; Yang, Xiaodong; Alomainy, Akram; Xidian University; University of Bedfordshire; et al. (IEEE, 2017-10-02)
      A method to locate the position of the user in an indoor environment employing Bayesian theory is presented in this paper. A detailed analysis of the positional accuracy is carried out evaluating effects of two major degradation factors namely the measurement and calculation errors. The proposed technique makes use of the Gaussian distribution of random data in indoor Zigbee propagation model based on received signal strength indicator (RSSI) and the triangular positioning algorithm, maximum likelihood estimation (MLE) and Bayesian theory. It identifies the user's location calculating the maximum probability point. The proposed method offers high accuracy levels with a mean error of 0.1363m as compared to the mean error values of 1.4059m and 0.4291m for the triangular localization and triangular localization with MLE methods, respectively.