An improved algorithm of Bayesian fingerprint localization based on bluetooth

doi:10.13474/j.cnki.11-2246.2019.0138

Abstract

Abstract: Bayesian estimation is an important position fingerprint localization algorithm, but the traditional equivalent Bayesian prior probability is not applicable in dynamic localization. In view of this problem, an improved algorithm based on Bayesian fingerprint localization is proposed in this paper. Firstly, by means of the heading information obtained by gyroscope and Gaussian kernel function model, the probabilistic voting algorithm is established to calculate the prior probability. Then, the prior probability combined with the signal strength is used to calculate the posterior probability of the point to be measured at the reference point. Finally, the most probable reference point is selected and the most probable value is calculated with the probability as the weight. In the regular path experiment, the average positioning error of the improved algorithm is 1.15 m, and the probability of positioning error less than 2 m is 96.1%. In the irregular path experiment, the average positioning error is 0.50 m, and the reliability of positioning error is 94.8%. In addition, the improved algorithm can improve the location hopping phenomenon and has good robustness.

Key words: indoor positioning, low energy bluetooth, Bayesian, gyroscopic heading, Gaussian kernel function, probabilistic voting algorithm

CLC Number:

P228

GUO Ying, FENG Mingyang, SUN Yuxi, JI Xianlei, LIU Qinghua. An improved algorithm of Bayesian fingerprint localization based on bluetooth[J]. 测绘通报, 2019, 0(5): 1-6.

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