A novel CSI-based fingerprinting for localization with a single AP

Zhang, Lei; Ding, Enjie; Hu, Yanjun; Ya-feng, Liu

doi:10.1186/s13638-019-1371-y

Cited by 31 publications

(20 citation statements)

References 35 publications

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“…In the case of non-moving objects, the multipath propagation is different depending on the location or the shape of objects where they exist between antennas. This characteristic is used for location estimation and object identification [6][7][8][9][10] among others. In these applications, the main classification algorithms for estimation, such as SVMs and convolutional neural networks (CNNs), are used.…”

Section: Related Studiesmentioning

confidence: 99%

“…The CSI consists of frequency and space information, expresses the effect of multipath propagation, and corresponds to the space state between transmitting and receiving antennas. To date, CSI has been used for various studies such as human movement detection, human behavior classification, location estimation, object identification, human counting in a room, and human counting in a passageway [3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine

Ogawa

Munetomo

2020

Sensors

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This paper proposes a channel state information (CSI)-based prediction method of a human flow that includes activity. The objective of the paper is to predict a human flow in an outdoor road. This human flow prediction is useful for the prediction of the number of passing people and their activity without privacy issues as a result of the absence of any camera systems. In this paper, we assume seven types of activities: one, two, and three people walking; one, two, and three people running; and one person cycling. Since the CSI can effectively express the effect of multipath fading in wireless signals, we expected the CSI to predict the various activities. In our proposed method, the amplitude and phase components are extracted from the measured CSI. The feature values for machine learning are the mean and variance of the maximum eigenvalue derived from the auto-correlation matrix and variance–covariance matrix composed of the amplitude or phase components and the passing time of flow. Using these feature values, we evaluated the prediction accuracy by leave-one-out cross-validation with a linear support vector machine (SVM). As a result, the proposed method achieved the maximum prediction accuracy of 100% for each direction and 99.5% for two directions.

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Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine

Ogawa

Munetomo

2020

Sensors

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“…And z i,j,n represents the corresponding channel noise. Based on [37], CSI i,j,n is further defined as follows…”

Section: Preliminary a Csi-mimomentioning

confidence: 99%

Learning Spatiotemporal Features of CSI for Indoor Localization With Dual-Stream 3D Convolutional Neural Networks

Yuan

Hao

2019

IEEE Access

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Recently, the research of WiFi-based indoor localization combing with deep-learning techniques has earned wide attention due to its potential applications in smart cities. In this paper, a novel fingerprinting system is proposed to achieve indoor localization via learning spatiotemporal features from channel state information (CSI) of multiple-input multiple-output wireless channels (CSI-MIMO) by a dual-stream three-dimensional (3D) convolutional neural network (DS-3DCNN). In the proposed system, the gathered raw CSI-MIMO data are firstly preprocessed through amplitude outliers elimination and phase sanitization for constructing a pair of 3D CSI-MIMO matrices including a 3D amplitude matrix and a 3D phase matrix. Next, the 3D matrices will be input to the DS-3DCNN deep neural network which consists of two parallel subnetworks with specific architecture of several convolution, batch normalization, maxpooling, and fully connected layers. Through this DS-3DCNN network, learning spatiotemporal features of CSI-MIMO is carried out simultaneously from 3D amplitude and phase matrices. And then, probabilistic classification results of two subnetworks are fused in the final output layer of the proposed DS-3DCNN based on Bayes' theorem. Moreover, in the offline training stage, a dual-stream joint optimization method is presented for efficiently optimizing network parameters. After offline training of the DS-3DCNN, in the online locating stage, current CSI-MIMO data are firstly collected from the mobile device to be located. Then probabilistic classification results are obtained from the output layer of the DS-3DCNN, and further used to approximate the posterior distribution of the mobile device's location with a Gaussian mixture model. Finally, a novel location estimation algorithm is deduced based on the minimum mean square error (MMSE) criterion. Since unique features of wireless MIMO channels are jointly learnt in spatial, temporal, and frequency domains, the proposed DS-3DCNN based fingerprinting system is reasonable to provide accurate localization results in indoor environments, which is verified in corresponding experiments. INDEX TERMS Indoor localization, deep learning, convolutional neural network (CNN), channel state information (CSI).

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“…The fingerprint-based localization method has been widely used in indoor localization systems because it does not need to understand the relationship between distance and signal and has a relatively high accuracy [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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

Dang

Tang

Hao

et al. 2020

J Wireless Com Network

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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.

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A novel CSI-based fingerprinting for localization with a single AP

Cited by 31 publications

References 35 publications

Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine

Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine

Learning Spatiotemporal Features of CSI for Indoor Localization With Dual-Stream 3D Convolutional Neural Networks

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

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