CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection

Han, Chong; Tan, Qingqing; Sun, Lijuan; Zhu, Hai; Guo, Jian

doi:10.3390/info9040095

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…The fingerprint method is a common method for Wi-Fi indoor positioning [26][27][28][29][30][44][45][46][47][48][49], and various factors affect positioning results [50,51]. In this paper, we discuss the effects of three typical user poses and body shadowing on Wi-Fi signals.…”

Section: Discussionmentioning

confidence: 99%

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

et al. 2019

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The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint matching process, user poses and user body shadowing have serious impact on the received signal strength (RSS) data and degrade matching accuracy; however, this impact has not attracted large attention. In this study, we systematically investigate the impact of user poses and user body shadowing on the collected RSS data and propose a new method called the pose recognition-assisted support vector machine algorithm (PRASVM). It fully exploits the characteristics of different user poses and improves the support vector machine (SVM) positioning performance by introducing a pose recognition procedure. This proposed method firstly establishes a fingerprint database with RSS and sensor data corresponding to different poses in the offline phase, and fingerprints of different poses in the database are extracted to train reference point (RP) classifiers of different poses and a pose classifier using an SVM algorithm. Secondly, in the online phase, the poses of RSS data measured online are recognised by a pose classifier, and RSS data measured online are grouped with different poses. Then online RSS data from each group at an unknown user location are reclassified as corresponding RPs by the RP classifiers of the corresponding poses. Finally, user location is determined by grouped RSS data corresponding to coordinates of the RPs. By considering the user pose and user body shadowing, the observed RSS data matches the fingerprint database better, and the classification accuracy of grouped online RSS data is remarkably improved. To verify performances of the proposed method, experiments are carried out: one in an office setting, and the other in a lecture hall. The experimental results show that the positioning accuracies of the proposed PRASVM algorithm outperform the conventional weighted k-nearest neighbour (WKNN) algorithm by 52.29% and 40.89%, outperform the SVM algorithm by 73.74% and 60.45%, and outperform the pose recognition-assisted WKNN algorithm by 34.76% and 21.86%, respectively. As a result, the PRASVM algorithm noticeably improves positioning accuracy.

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Section: Discussionmentioning

confidence: 99%

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

et al. 2019

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“…The k-weighted nearest node (KWNN) method [18] and another heat map [19] method utilizing similarities between access points will improve the accuracy of the localization. Many other fingerprinting approaches are [20][21][22][23][24][25]. The methods of fingerprinting can achieve up to 60 cm of high precision.…”

Section: Fig 2 -Technology Of Indoor Fingerprint Positioning Based On...mentioning

confidence: 99%

Wi-Fi Fingerprinting for Indoor Positioning

Ali¹,

Kamardin²,

Maidin³

et al. 2022

IJIE

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Wireless Fidelity (Wi-Fi) fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since most commercial infrastructure is well equipped with Wi-Fi routers. The use of Wi-Fi fingerprinting techniques is becoming more common in the field of indoor positioning systems (IPS). For the purpose of indoor location localization, each method for obtaining a Wi-Fi fingerprint has been analyzed and discussed in this work. In this study, the majority of the algorithms that are associated with Wi-Fi fingerprinting have been interpreted, and the earlier works of other researchershave been critically evaluated, in order to get a more in-depth understanding of how the Wi-Fi fingerprinting process works.

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“…FCC observes the variation in CSI and its relation with the number of moving people. Han et al (2018) leveraged CSI to track human motion in an indoor environment using the multiple antenna voting method. A home intruder system based on the CSI of WiFi signals was also presented (Al-qaness et al 2016b).…”

Section: Csimentioning

confidence: 99%

Device-free human micro-activity recognition method using WiFi signals

Al-qaness

2019

Geo-spatial Information Science

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Human activity tracking plays a vital role in human-computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro-and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based microactivity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.

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CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection

Cited by 12 publications

References 24 publications

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

Wi-Fi Fingerprinting for Indoor Positioning

Device-free human micro-activity recognition method using WiFi signals

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