Open AccessPosted Content
CRISLoc: Reconstructable CSI Fingerprintingfor Indoor Smartphone Localization.
TLDR
In this paper, the authors presented CRISLoc, the first CSI fingerprinting based indoor localization prototype system using ubiquitous smartphones, which operates in a completely passive mode, overhearing the packets on-the-fly for his own CSI acquisition.Abstract:
Channel state information (CSI) based fingerprinting for WIFI indoor localization has attracted lots of attention very recently.The frequency diverse and temporally stable CSI better represents the location dependent channel characteristics than the coarsereceived signal strength (RSS). However, the acquisition of CSI requires the cooperation of access points (APs) and involves only dataframes, which imposes restrictions on real-world deployment. In this paper, we present CRISLoc, the first CSI fingerprinting basedlocalization prototype system using ubiquitous smartphones. CRISLoc operates in a completely passive mode, overhearing thepackets on-the-fly for his own CSI acquisition. The smartphone CSI is sanitized via calibrating the distortion enforced by WiFi amplifiercircuits. CRISLoc tackles the challenge of altered APs with a joint clustering and outlier detection method to find them. A novel transferlearning approach is proposed to reconstruct the high-dimensional CSI fingerprint database on the basis of the outdated fingerprintsand a few fresh measurements, and an enhanced KNN approach is proposed to pinpoint the location of a smartphone. Our studyreveals important properties about the stability and sensitivity of smartphone CSI that has not been reported previously. Experimentalresults show that CRISLoc can achieve a mean error of around 0.29m in a6m times 8mresearch laboratory. The mean error increases by 5.4 cm and 8.6 cm upon the movement of one and two APs, which validates the robustness of CRISLoc against environment changes.read more
Citations
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Journal ArticleDOI
CSI Fingerprinting Localization With Low Human Efforts
TL;DR: A Wi-Fi localization scheme based on channel state information (CSI) of wireless signals, which manages to relieve time-consuming site survey and improves localization accuracy by parsing the user’s trajectory instead of restricting to the single spot.
Journal ArticleDOI
Multiview Variational Deep Learning With Application to Practical Indoor Localization
TL;DR: A view-selective deep learning system for indoor localization using CSI of WiFi, the first approach to apply variational inference and to construct a practical system for radio localization, and a methodology for supervised learning with multiview data where informative and noninformative views coexist.
Journal ArticleDOI
Machine Learning for Practical Localization System Using Multiview CSI
TL;DR: This article introduces a novel design of a signal preprocessing method for NN fingerprinting that 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.
Journal ArticleDOI
Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence
P. Surendra Varma,Veena Anand +1 more
TL;DR: In this article , the authors proposed a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location.
Proceedings ArticleDOI
Transfer Learning to adapt 5G AI-based Fingerprint Localization across Environments
Maximilian Stahlke,Tobias Feigl,Mario H. Castaneda Garcia,Richard Stirling-Gallacher,Jochen Seitz,Christopher Mutschler +5 more
TL;DR: A transfer learning (TL) method is proposed that exploits realistic synthetic Channel State Information obtained with the Quasi Deterministic Radio channel Generator (QuaDRiGa) used to pre-train the CNN-based fingerprint model so that it can be adapted to any real (NLoS) propagation scenario with a low number of real training samples.
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Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation
TL;DR: E elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision are demonstrated.
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