Robust Sub-Meter Level Indoor Localization - A Logistic Regression Approach

Xiang, Chenlu; Zhang, Zhichao; Zhang, Shunqing; Xu, Shugong; Cao, Shan; Lau, Vincent K. N.

doi:10.1109/icc.2019.8761091

Cited by 16 publications

(11 citation statements)

References 20 publications

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“…An unknown location was estimated as centroid of fingerprinted locations with weights computed from autoencoders' reconstruction errors. Besides the above classification-first localization methods, CSI measurements were trained directly to provide the coordinate estimation by formulating a regression problem in [11], [12].…”

Section: B Csi Fingerprintingmentioning

confidence: 99%

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

et al. 2020

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Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements. In this paper, we propose to use a mid-grained intermediate-level channel measurement-spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the IEEE 802.11ad/ay standardsto construct the fingerprinting database. These intermediate channel measurements are further utilized by a deep learning approach for multiple purposes: 1) location-only classification; 2) simultaneous locationand-orientation classification; and 3) direct coordinate estimation. Furthermore, the effectiveness of the framework is thoroughly validated by an in-house experimental platform consisting of 3 access points using commercial-off-the-shelf millimeter-wave WiFi routers. The results show a 100% accuracy if the location is only interested, about 99% for simultaneous location-and-orientations classification, and an averaged root mean-square error (RMSE) of 11.1 cm and an average median error of 9.5 cm for direct coordinate estimate, greater than 2-fold improvements over the RMSE of 28.7 cm and median error of 23.6 cm for RSSI-like single SNR-based localization.

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Section: B Csi Fingerprintingmentioning

confidence: 99%

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

et al. 2020

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“…[10] fingerprinted full CSI over multiple time instants, calibrated their phases, and fitted one autoencoder for one location. CSI measurements can also be directly trained to regress the coordinate [11], [12]. More recently, [59] used annotations from camera images to train fine-grained CSI measurements for pose and human tracking.…”

Section: B Csi: Channel State Informationmentioning

confidence: 99%

Multi-Band Wi-Fi Sensing with Matched Feature Granularity

Yu¹,

PU²,

Wang³

et al. 2021

Preprint

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Complementary to the fine-grained channel state information (CSI) from the physical layer and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes (e.g., beam SNR) that are available at millimeter-wave (mmWave) bands during the mandatory beam training phase can be repurposed for Wi-Fi sensing applications. In this paper, we propose a multi-band Wi-Fi fusion method for Wi-Fi sensing that hierarchically fuses the features from both the fine-grained CSI at sub-6 GHz and the mid-grained beam SNR at 60 GHz in a granularity matching framework. The granularity matching is realized by pairing two feature maps from the CSI and beam SNR at different granularity levels and linearly combining all paired feature maps into a fused feature map with learnable weights.To further address the issue of limited labeled training data, we propose an autoencoder-based multi-band Wi-Fi fusion network that can be pre-trained in an unsupervised fashion. Once the autoencoder-based fusion network is pre-trained, we detach the decoders and append multi-task sensing heads to the fused feature map by fine-tuning the fusion block and re-training the multi-task heads from the scratch. The multi-band Wi-Fi fusion framework is thoroughly validated by in-house experimental Wi-Fi sensing datasets spanning three tasks: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to four baseline methods (i.e., CSI-only, beam SNR-only, input fusion, and feature fusion) demonstrates the granularity matching improves the multi-task sensing performance. Quantitative performance is evaluated as a function of the number of labeled training data, latent space dimension, and fine-tuning learning rates.

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“…1. In addition, we consider K + 1 multiple fading paths in this environment, and the channel responses H i (L, n) is assumed to remain 1 The proposed approach is equally applicable to single antenna users by extending the received signals to N R copies. constant during the transmission of the n th OFDM symbol, which is given by [19],…”

Section: A Channel Modelmentioning

confidence: 99%

Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point—Regression Versus Classification

Xiang

Zhang

et al. 2019

IEEE Access

Self Cite

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Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cramér-Rao lower bound (CRLB) assisted robust training, which achieves more robust submeter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.

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Robust Sub-Meter Level Indoor Localization - A Logistic Regression Approach

Cited by 16 publications

References 20 publications

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

Multi-Band Wi-Fi Sensing with Matched Feature Granularity

Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point—Regression Versus Classification

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