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

Xiang, Chenlu; Zhang, Shunqing; Xu, Shugong; Chen, Xiaojing; Cao, Shan; Alexandropoulos, George C.; Lau, Vincent K. N.

doi:10.1109/access.2019.2946271

Cited by 42 publications

(27 citation statements)

References 28 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%

“…Given its ubiquitous presence, WiFi stands out as a technology for infrastructure-free indoor localization. Most WiFi-based indoor localization frameworks use either fine-grained channel state information (CSI) from the physical layer [3]- [12] or coarse-grained RSSI measurements from the MAC layer [13]- [29] for fingerprinting or direct localization; see more detailed literature review in the next section.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…To address this problem, especially after the deep learning technique has been invented, the model-free localization approaches, such as restricted Boltzmann machine (RBM) [47], Multilayer Perceptron (MLP) [57] and convolutional neural networks (CNN) [48] have been applied to exploit fingerprints features and classify to different reference positions with certain probability. According to the latest literature, complex neural network structures has made it to improve the positioning accuracy to the sub-meter, even decimeter level with controllable online prediction overhead.…”

Section: Fig 5 Cdf Of Localization Errors For Different Algorithmsmentioning

confidence: 99%

“…According to the latest literature, complex neural network structures has made it to improve the positioning accuracy to the sub-meter, even decimeter level with controllable online prediction overhead. In addition, it is also proven that the similar neural networks, applied to treat the localization tasks as logistic regression tasks rather than classification tasks, can obtain more robust positioning accuracy at non-RP positions [48]. In order to show the performance improvements of AI-assisted localization algorithms more vividly, we summarize the localization accuracy performance results of the existing literature in Fig.…”

Section: Fig 5 Cdf Of Localization Errors For Different Algorithmsmentioning

confidence: 99%

6G: Connecting Everything by 1000 Times Price Reduction

Zhang

Xiang

2020

IEEE Open J. Veh. Technol.

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The commercial deployment of 5G communication networks makes the industry and academia to seriously consider the possible solutions for the next generation. Although the conventional approach indicates that 6G vision and requirements can be figured out by simply multiplying a certain amount of magnitude on top of the previous generations, we argue in this article that 1000 times price reduction from the customer's view point is the key to success. Guided by this vision, we categorize the current candidate technologies in a well organized manner and select AI-assisted intelligent communications as an example to elaborate the drive-force behind. Although it is impossible to identify every detail of 6G during the current time frame, we believe this article will help to eliminate the technical uncertainties and aggregate the efforts towards key breakthrough innovations for 6G.

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“…According to the obtained median accuracy result (50th percentile value), which is below 1 m, the proposed system is able to provide a more robust performance leading to the achievement of a sub-meter level of localization accuracy. This definition of sub-meter accuracy has been adopted from [24] and [25]. Therefore, the main contribution of this work rests on the mitigation of the body shadowing, returning the UWB accuracy to below 1m, regardless the presence of the human body that carries the UWB TAG.…”

Section: Figurementioning

confidence: 99%

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

et al. 2020

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Thanks to its ability to provide sub-meter level positioning accuracy, Ultrawideband (UWB) has found wide use in several wireless body area network (WBAN) applications such as ambient assisted living, remote patient management and preventive care, among others. In spite of the attractiveness of UWB, it is not possible to achieve this level of accuracy when the human body obstructs the wireless channel, leading to a bias in the Time of Flight (TOF) measurements, and hence a detection of position errors of several meters. In this paper, a study of how a sub-meter level of accuracy can be achieved after compensating for body shadowing is presented. Using a Particle Filter (PF), we apply UWB ranging error models that take into consideration the body shadowing effect and evaluate them through simulations and extensive measurements. The results show a significant reduction in the median position error of up to 75 % and 82 % for simulations and experiments, respectively, leading to the achievement of a sub-meter level of localization accuracy. INDEX TERMS Body wearable sensors, Human body shadowing, Localization, Ranging, Ultrawideband, Time of Flight, Particle Filter I. INTRODUCTION With rapid developments of computer and miniaturization technologies, wearable sensors are becoming an important part of our daily lives. With a market expected to grow to $ 70 billion in 2025 [1], wearables are widely used in several applications such as in sport science, rehabilitation, medical monitoring, surveillance, among others [2]-[4]. Therefore, they can be attached to shoes, eyeglasses, earrings, clothing, gloves and watches, etc of the user of the wearable. Collecting precise user localization information is one of the salient capabilities of wearables [5]. In fact, according to [6], a sub-meter level of localization accuracy is envisaged for several wireless body area networks (WBAN). Currently, Global Navigation Satellite System (GNSS) is the most widely spread localization technology in outdoor environments.

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

Cited by 42 publications

References 28 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

6G: Connecting Everything by 1000 Times Price Reduction

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

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