CSI-based fingerprinting for indoor localization using LTE Signals

Pecoraro, Giovanni; Domenico, Simone Di; Cianca, Ernestina; Sanctis, Mauro De

doi:10.1186/s13634-018-0563-7

Cited by 43 publications

(49 citation statements)

References 26 publications

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“…LTE signals have almost seamless coverage everywhere, which can be used in wireless induction as an easy-to-receive signal source. The movement of the human body may cause a change in the CSI of the LTE signals so that LTE signals can help in human sensing [32,33]. LTE is also suitable for the fingerprint algorithm to realize human localization [34,35].…”

Section: Ltementioning

confidence: 99%

“…Template matching recognition is often training-free [10,19,20,39,55,74,91,93,94]. Training-once classification requires the valid features robust to the variations in the surrounding environment [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,90,92,127,130]. Deep learning automatically extracts features, which often requires only one time of training [16,17,21,48,59,67,77,88,95,96,108].…”

Section: Activity Classificationmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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Section: Ltementioning

confidence: 99%

Section: Activity Classificationmentioning

confidence: 99%

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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“…The "shape" of the channel frequency response (CFR) could be used to construct a CSI-based fingerprint database. It reduced the memory requirement of the database and the computational complexity of the matching phase [35]. In summary, current deep-learning-based fingerprint positioning approaches focus on developing indoor and small-scale outdoor positioning technologies.…”

Section: Related Workmentioning

confidence: 99%

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Lei

Zhang

2020

Sensors

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In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.technologies require additional devices, making these systems impractical. Additionally, in signal NLOS propagation, the performance of the positioning systems will be greatly reduced [5].In contrast, wireless fingerprint positioning technology has received much attention owing to its simplicity and practicality with existing infrastructure and hardware. Many complex LTE signal cues are hidden in the surrounding environment, and the goal of wireless fingerprint-based localization is to discover these cues and use them effectively to determine UE positions [6]. Compared to satellite navigation positioning systems and other wireless positioning technologies, fingerprint-based positioning techniques have many merits. First, low-energy consumption sensors in UE require much less energy. Second, most of the fingerprint-based positioning technologies require no additional devices or infrastructure. Finally, fingerprint-based positioning can be achieved by using ubiquitous smartphones and signals from LTE base stations (BSs) [6,7]. Fingerprint positioning technology works in two phases: an offline training phase and an online matching phase [8]. During the training phase, signal measurements can be used, which are usually received signal strength indicator (RSSI), reference signal receivin...

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“…Through this approach, we have M locations {L m } and the corresponding measured CSIs {H(L m , t)}, which can be used as supervised information to train the SLN. Once the training of SLN is finalized, we can use the trained network to model g 1 (·) and obtainL ti m for the m th period according to (3). Together with M locations {L m }, we have the training data sets for FN.…”

Section: B Training Data Setsmentioning

confidence: 99%

“…Localization has been identified as one of the most popular applications in the modern society, especially when massive amounts of devices are connected with each other [1]. Various types of services, including autonomous driving [2] and indoor navigation [3], [4], require high resolution localization information in outdoor and indoor environments, which motivates continuous research interests in recent years [3]- [5]. Although Global Navigation Satellite Systems (GNSSs), such as Global Positioning System (GPS), can provide continuous localization information in the outdoor environments, the performance degradation usually happens when the satellite signals are blocked, e.g.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint-Based Localization Using Commercial LTE Signals: A Field-Trial Study

Zhang

et al. 2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively.

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CSI-based fingerprinting for indoor localization using LTE Signals

Cited by 43 publications

References 26 publications

Human Activity Sensing with Wireless Signals: A Survey

Human Activity Sensing with Wireless Signals: A Survey

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Fingerprint-Based Localization Using Commercial LTE Signals: A Field-Trial Study

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