Accurate Indoor Localization Based on CSI and Visibility Graph

Wu, Zhefu; Jiang, Lei; Jiang, Zhuangzhuang; Chen, Bin; Liu, Kai; Xuan, Qi; Xiang, Yun

doi:10.3390/s18082549

Cited by 26 publications

(24 citation statements)

References 33 publications

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“…This work yields an error distance of 0.95 m with a single access point. Authors in [27] used a visibility graph to transform the CSI amplitude and phase information of individual sub-carriers into complex networks extracting the network features accordingly. Then, machine learning algorithms are applied to infer the locations, yielding a 96% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

et al. 2020

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In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.

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Section: Related Workmentioning

confidence: 99%

Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

et al. 2020

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“…Support vector regression (SVR) based system was introduced in [24] for device-free localization which [25], [26] also considered. There are other various learning techniques for localization, such as autoencoder [27], clustermapping (C-Map) [28], kNN [29], multi-layer perceptron (MLP), [30], canonical correlation analysis (CCA) [31], and visibility graph (VG) [32].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Practical Localization System Using Multiview CSI

2020

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Various machine learning techniques on indoor localization using radio signals are being rapidly developed to achieve a sub-meter accuracy under noisy and complex environments. A fingerprint database using channel state information (CSI) extracted from a radio packet based on an orthogonal frequency diversity multiplexing (OFDM) channel can provide enough information to localize a transmitter device with a neural network (NN) based machine learning technique. In this paper, we concern about the more practical use of the localization system using machine learning. We introduce a novel design of a signal preprocessing method for NN fingerprinting. To deal with the real building environment with corridors where certain signals cannot arrive at the receiver, our preprocessing with nonnegative matrix factorization (NMF) recovers multiview CSI of the original signal and complete the sparse CSI matrix, which enables robust and practical localization. The recovered CSI is then applied to variational inferencebased machine learning that finds informative corridor views among multiview CSI. Our proposed system significantly outperforms other existing machine learning-based systems and shows a localization accuracy of 89 cm, while it still maintains the reliable accuracy even with 30 % sparse network. It is the first time to consider how to design a practical localization system in an empirical building environment.

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“…However, the accurately calibration of Wi-Fi fingerprints remains a question due to the inherent instability of the real world. Recently, channel state information (CSI) based indoor localization [36,37] have attracted much attention. When compared with RSS-based solution, CSI maintains more stability and sensitivity, which can provide detailed and fine-grain subcarrier information.…”

Section: Introductionmentioning

confidence: 99%

“…When compared with RSS-based solution, CSI maintains more stability and sensitivity, which can provide detailed and fine-grain subcarrier information. For example, in [36], a CSI based indoor localization technique is developed by employing both the intrasubcarrier statistics features and the inter-subcarrier network features. Their results showed that it could achieve 96% classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

Liu

Zhang

et al. 2019

Remote Sensing

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One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.

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Accurate Indoor Localization Based on CSI and Visibility Graph

Cited by 26 publications

References 33 publications

Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

Machine Learning for Practical Localization System Using Multiview CSI

An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

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