RSS Missing Value Estimation with Generative Adversarial Networks Model

doi:10.13190/j.jbupt.2019-210

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (1): 26-31.doi: 10.13190/j.jbupt.2019-210

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RSS Missing Value Estimation with Generative Adversarial Networks Model

REN Xiao-qi, OWN Chung-ming

College of Intelligence and Computing, Tianjin University, Tianjin 300350, China

Received:2019-11-09 Online:2021-02-28 Published:2021-09-30

Abstract

Abstract: Wireless-fidelity(Wi-Fi)positioning is currently the mainstream method in indoor positioning,and the construction of fingerprint database is the key to Wi-Fi positioning system. However,the received signal strength(RSS)value in the fingerprint database will be changed with the variability of the indoor environment,and it is usually need to constantly re-measure the value in the fingerprint database,which leads to high cost and long time,especially in the dynamic environment with large positioning area. To address this problem,the adaptive context generative adversarial networks model is proposed. The model only needs to measure part of RSS fingerprints,and then learn the RSS fingerprints distribution to finally predict the missing fingerprint at a specific location. Simulation shows that the accuracy of indoor positioning is significantly improved,and the labor cost is greatly reduced.

Key words: received signal strength fingerprint database, generative adversarial network, indoor positioning system

CLC Number:

TN932

REN Xiao-qi, OWN Chung-ming. RSS Missing Value Estimation with Generative Adversarial Networks Model[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 26-31.

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RSS Missing Value Estimation with Generative Adversarial Networks Model

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