Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

Xiaochao Dang,Jiaju Ren,Zhanjun Hao,Xuhao Tang

doi:10.1186/s13638-020-01692-7

Xiaochao Dang, Jiaju Ren + Show 2 more

Open Access

https://doi.org/10.1186/s13638-020-01692-7

Copy DOI

Abstract

The fingerprint indoor localization method based on channel state information (CSI) has gained widespread attention. However, this method fails to provide a better localization effect and higher localization accuracy due to poor fingerprint accuracy, unsatisfactory classification and matching effect, and vulnerability to environmental impacts. In order to solve the problem, this paper proposes a CSI fingerprint indoor localization method based on the Discrete Hopfield Neural Network (DHNN). The method mainly consists of off-line and on-line phases. At the off-line phase, a low-pass filter is applied to conduct a preliminary processing on the fingerprint information of each reference point, and then, phase difference is adopted to correct the fingerprint data of all reference points. In this way, the quality of fingerprint data is improved, hence avoiding problems such as indoor environmental changes and multipath effect of signals, etc. in which impact the fingerprint data. Finally, the characteristic fingerprint database is established after acquiring relatively accurate fingerprint data. At the on-line phase, to maintain the consistency of data, the data of each reference point in the fingerprint database is set as an attractor. Meanwhile, the localization information of the test point is processed to make convergence judgment through DHNN. Eventually, the localization result is obtained. The experimental results show that the localization accuracy with a median error of 1.6 m can be achieved through the proposed method in the experimental environment. Compared with similar methods, it has a higher stability which can significantly reduce the cost of manpower and time.

Highlights

With the increasing popularity of location-based service (LBS), the demand for localization-based services in life is increasing [1]
The widespread popularity of indoor Wi-Fi has given rise to various indoor localization technologies based on Wi-Fi [3], which includes localization based on the received signal strength indication (RSSI) and channel state information (CSI)
Combining with the problems existing in the above methods, and in order to achieve high accuracy and reliability of indoor location, this paper proposes a CSI fingerprint indoor localization method based on Discrete Hopfield Neural Network (DHNN)

Summary

Introduction

With the increasing popularity of location-based service (LBS), the demand for localization-based services in life is increasing [1]. Some localization methods only include the original phase information and do not process the phase information, but the original phase information cannot describe the corresponding position information well, and include the position information some noise effects due to the environment It cannot reflect the integrity of the data, and the comprehensiveness of location information is missing. Combining with the problems existing in the above methods, and in order to achieve high accuracy and reliability of indoor location, this paper proposes a CSI fingerprint indoor localization method based on Discrete Hopfield Neural Network (DHNN). (1) The design uses a combination of low-pass filter and phase difference correction to process the fingerprint information, ensuring the accuracy of the fingerprint data and greatly reducing the localization error.

Preliminary

DHNN model design

Experimental setup and analysis

Performance results and discussion

Findings

Conclusions