Passive Indoor Localization Based on CSI and Naive Bayes Classification

Wu, Zhefu; Xu, Qiang; Li, Jianan; Fu, Chenbo; Xuan, Qi; Xiang, Yun

doi:10.1109/tsmc.2017.2679725

Cited by 118 publications

(70 citation statements)

References 36 publications

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“…In these methods, a target device receives a signal from each AP (access point) with known position of the AP, detects signal propagation distance and AoA (angle of arrival) from information, such as signal strength/phase, and on this basis computes its own position [13][14][15][16]. In addition, device-free methods [17][18][19][20] for detecting a person without any wearable device are also proposed, which monitor the changes in signal reception status between pairs of preset devices.…”

Section: Indoor Positioning Via Radio Wavesmentioning

confidence: 99%

Radiation Angle Estimation and High-Precision Pedestrian Positioning by Tracking Change of Channel State Information

Komamiya

Tang

Obana

2020

Sensors

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P2V (pedestrian-to-vehicle) communication, in which a pedestrian’s mobile device notifies its position to nearby vehicles in order to prevent pedestrian accidents, has attracted much research interest recently, but its performance largely depends on the precision of pedestrians’ positioning. Pedestrian positioning is generally performed by using GPS (Global Positioning System), and its precision may greatly degrade in urban canyons. To improve positioning precision of pedestrians, it was proposed that vehicles around pedestrians be used as anchors beside satellites. In this method, a pedestrian device overhears V2V (vehicle-to-vehicle) communication signals which carry the vehicle position and calculates a pedestrian’s position using pedestrian–vehicle distance/angle information estimated from CSI (channel state information) of the V2V signals. However, angle estimation typically depends on the number of antennas of the pedestrian device. In this paper, we propose a new method to estimate signal radiation angle by tracking temporal change of CSI caused by vehicle movement during signal transmission and investigate its application to precise pedestrian positioning. Three-dimensional ray tracing simulations confirm that compared to the base method using eight antennas, the proposed method with a single antenna reduces average angle error from 13 degrees to 3.9 degrees, and reduces average positioning error from 2.49 m to 0.78 m.

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Section: Indoor Positioning Via Radio Wavesmentioning

confidence: 99%

Radiation Angle Estimation and High-Precision Pedestrian Positioning by Tracking Change of Channel State Information

Komamiya

Tang

Obana

2020

Sensors

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“…Naive Bayes classification method depends on two basic assumptions, first the features are independent from one another. Second, each feature has the same prominence [17]. With these assumptions, Naive Bayes algorithm works based on an existing probability to determine future probabilities.…”

Section: Naïve Bayesmentioning

confidence: 99%

Levels of Political Participation Based on Naive Bayes Classifier

Hidayatillah

Mirwan

Hakam

et al. 2019

Indonesian J. Comput. Cybern. Syst.

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Nowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has been known as a political microblogging media that can provide data about current political event based on users’ tweets. By using Twitter as a data source, this study analyzes public participation during campaign period for 2018 Central Java regional head election. The purpose is to observe how much reaction is given to each candidate who advanced in the election. By using the crawling program, all tweets containing certain candidate names will be downloaded. After going through a series of preprocessing stages, data can be classified using Naive Bayes. Predictor features in classification datasets are the number of replies, retweets, and likes. While the target variable is reaction that is divided into three levels, including high, medium, and low. These levels are determined based on users’ reaction in a tweet. By using these rules, Naive Bayes managed to classify data correctly as much as 76.74% for Ganjar Pranowo and 68.81% for Sudirman Said.

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“…Figure 2 shows that when the transmitting antenna and the receiving antenna are in the vicinity of the human body, the signal energy attenuation mainly occurs because of the path loss caused by the body surface crawling wave. As the transmitting antenna gradually moves away from the human body, the signal energy attenuation path loss mainly occurs from air propagation [26,27]. Therefore, there is a critical point between the human body and the antenna for waves under human occlusion.…”

Section: Related Workmentioning

confidence: 99%

A Signal Recovery Method Based on Bayesian Compressive Sensing

Hao

Dang

2019

Mathematical Problems in Engineering

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In a precise positioning system, weak signal errors caused by the influence of a human body on signal transmission in complex environments are a main cause of the reduced reliability of communication and positioning accuracy. Therefore, eliminating the influence of interference from human crawling waves on signal transmissions in complex environments is an important task in improving positioning systems. To conclude, an experimental environment is designed in this paper and a method using the Ultra-Wideband (UWB) Local Positioning System II (UWB LPS), called Bayesian Compressed Sensing-Crawling Waves (BCS-CW), is proposed to eliminate the impact of crawling waves using Bayesian compressive sensing. First, analyse the transmission law for crawling waves on the human body. Second, Bayesian compressive sensing is used to recover the UWB crawling wave signal. Then, the algorithm is combined with the maximum likelihood estimation and iterative approximation algorithms to determine the label position. Finally, through experimental verification, the positioning accuracy of this method is shown to be greatly improved compared to that of other algorithms.

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Passive Indoor Localization Based on CSI and Naive Bayes Classification

Cited by 118 publications

References 36 publications

Radiation Angle Estimation and High-Precision Pedestrian Positioning by Tracking Change of Channel State Information

Radiation Angle Estimation and High-Precision Pedestrian Positioning by Tracking Change of Channel State Information

Levels of Political Participation Based on Naive Bayes Classifier

A Signal Recovery Method Based on Bayesian Compressive Sensing

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