Device-Free Orientation Detection Based on CSI and Visibility Graph

Wu, Zhefu; Pan, Xingda; Fan, Kunpeng; Li, Kai; Xiang, Yun

doi:10.1109/tsmc.2019.2933161

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…Fingerprint/Application Method CSI-based FIFS [17] Amplitude, Phase Bayes DeepFi [18] Amplitude Deep Network FapFi [19] Amplitude, Phase Bayes BiLoc [20] Amplitude, Phase Bi-modal deep learning D. Man et al [21] Amplitude Weighted Naive Bayes Y. Wang et al [22] Amplitude RF Z. Wu et al [23] Amplitude Improved Bayes Z. Wu et al [24] Amplitude, Phase Time-Reversal (TR) C. Chen et al [25] Amplitude, Phase Time-Reversal (TR) VG-based L. Lacasa et al [27] Put forward the VG VG J. Iacovacci et al [28] Picture processing VG Z. Wu et al [29] Human body orientation detection VG Z. Wu et al [30] Indoor localization VG L. Lacasa et al [31] Put forward the HVG HVG S.S. Roy et al [32] Discharge detection HVG Y. Luo et al [33] Network traffic feature extraction LPVG Y. Lv et al [16] Muscle synergy MLPHVG grained characteristics can more accurately depict changes in channel states, which makes it highly attractive for realizing high-precision indoor localization technology. Therefore, this paper takes CSI fingerprint as the research subject.…”

Section: System/workmentioning

confidence: 99%

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Device-Free Indoor Localization of CSI Based on Limited Penetrable Horizontal Visibility Graph

Liu

2022

IEEE Access

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Most of the current indoor localization methods based on channel state information (CSI) utilize the amplitude and phase of each subcarrier individually as location fingerprints, but the correlation between adjacent subcarriers also contains essential location information. Therefore, this paper proposes a device-free indoor localization method based on CSI and limited penetrable horizontal visibility graph (LPHVG), which does not require the subject to carry a device and can be implemented with only a single access point (AP). Firstly, we model the frequency correlation between subcarriers by LPHVG algorithm and construct a CSI-based complex network. Secondly, the topology of the complex network is utilized to analyze the relationship between CSI adjacent subcarriers and extract network features, which are combined with statistical features as fingerprint information to characterize different locations. Finally, the method is combined with support vector regression (SVR) to realize indoor localization in different environments. The experimental results show that the proposed method can significantly improve the localization accuracy, and has an excellent performance in different indoor scenarios.

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Section: System/workmentioning

confidence: 99%

“…Reference [28] utilized VG algorithm for image processing. Reference [29] proposed to utilize VG to model the correlation between CSI subcarriers to achieve the detection of human orientation. Later, VG was suggested to be applied to indoor localization [30].…”

Section: B Visibility Graph Techniquesmentioning

confidence: 99%

Device-Free Indoor Localization of CSI Based on Limited Penetrable Horizontal Visibility Graph

Liu

2022

IEEE Access

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“…environments, and therefore it enables WiFi-based radar technology [2], [3]. WiFi-based radar can sense human motions by extracting CSI patterns by signal processing [4] or data-driven models [5], which has empowered many applications at smart homes including activity recognition [6], gesture recognition [7], human identification [8], [9], human-computer interface [10] and vital sign detection [4].…”

Section: Introductionmentioning

confidence: 99%

AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

Yang¹,

Chen²,

Zhao³

et al. 2022

Preprint

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WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and advanced machine learning models to analyze motion patterns in CSI.Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect highquality, well-segmented and balanced CSI samples in a new environment for adaptation algorithms, but randomly-captured CSI samples can be easily collected. In this paper, we firstly explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an automatic WiFi sensing model based on a novel geometric self-supervised learning algorithm. The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public datasets to further demonstrate its capacity for crosstask learning. For the UT-HAR and Widar datasets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that the AutoFi takes a huge step toward automatic WiFi sensing without any developer engagement while overcoming the cross-site issue.

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“…D EVICE-free localization (DFL) is a technology for detecting and tracking a human in indoor and outdoor environments without the need for any wireless devices in wireless sensor networks (WSNs) [1]- [3]. DFL has attracted a great deal of research attention in security and monitoring systems for indoor and outdoor areas, e.g., emergency rescue systems, security monitoring systems and health care systems [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

“…The monitoring area was divided into voxels. The Narrowband Relative Ultra-Wideband [10], [11], [12] UWB Relative Localization from RSS [4], [5], [6], [7], [13], [14], [15], [16] RSS Absolute Fingerprint-Matching [3], [17], [18], [19], [20] RSS Absolute Radio Tomographic Imaging [1], [2], [15], [21], [22], [23], [24], [33] RSS Absolute Support Vector Machine [25], [26], [27] RSS Absolute Bayesian system [16], [28], [29] RSS Relative Compressed Sensing [30], [31], [32] RSS Relative weightings of voxels inside one elliptical weight mode were the same, which was not consistent with the actual situation. Consequently, several researchers studied the elliptical weight model for improvement in localization estimation accuracy in RTI.…”

Section: Introductionmentioning

confidence: 99%

Device-Free Localization Using Enhanced Channel Selection and a Distance-Based Elliptical Model

Lei

2022

IEEE Access

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Device-free localization (DFL) based on wireless sensor networks (WSNs) is a technology that can detect and locate a person by measuring the changes in received signals without the need for any wireless devices. As an emerging important technology in WSNs, radio tomographic imaging (RTI) has received increasing attention. However, there is much room to improve localization accuracy in RTI. To address this issue, an enhanced channel-selection method and a new distance-based elliptical model are proposed to improve the localization accuracy. The enhanced frequency channel-selection method selects two channels with the lowest received signal strength (RSS) variances to collect data. This approach is more robust to environmental change. The new distance-based elliptical model is based on the distance between the voxels and sensors. Meanwhile, the communication links are divided into line-of-sight (LOS) paths and nonline-of-sight (NLOS) paths. Experimental results demonstrate that the proposed algorithm improves the accuracy of positioning by up to 44.8% over some state-of-the-art RTI methods with low cost.

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Device-Free Orientation Detection Based on CSI and Visibility Graph

Cited by 8 publications

References 38 publications

Device-Free Indoor Localization of CSI Based on Limited Penetrable Horizontal Visibility Graph

Device-Free Indoor Localization of CSI Based on Limited Penetrable Horizontal Visibility Graph

AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

Device-Free Localization Using Enhanced Channel Selection and a Distance-Based Elliptical Model

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