Deep Learning for Fingerprint-Based Outdoor Positioning via LTE Networks

Li, Da; Lei, Yingke

doi:10.3390/s19235180

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…Therefore, places of interest are divided into multiple grids, and each grid is regarded as one class ref. [24]. When constructing a fingerprint database, one person holding a collecting device walks around the grids and collects wireless signals.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…After this, in each grid, a series of fingerprint images based on the collected signal measurements was generated. The grid size largely determined the positioning accuracy, and therefore the grid size would not be too large [24].…”

Section: Long-term Evolution (Lte) Signal Collectionmentioning

confidence: 99%

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Lei

Zhang

2020

Sensors

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“…Data enhancement can be achieved by enlarging and randomly rotating the original images. 3 First, the fingerprint image is standardized to 224 × 224, which can make MResNet better learn the image features. Second, the picture is enlarged by 1.25 times, and then the original image is randomly rotated by 15 .…”

Section: Dnn Training Modulementioning

confidence: 99%

“…2 With the popularity of longtime-evolution (LTE) signals, by measuring the signal features, large-scale outdoor positioning with wireless signals becomes possible. 3 Traditional wireless positioning techniques can be divided into range-based techniques and range-free techniques. 4 The range-based positioning system determines the user equipment (UE) position based on the time of arrival (TOA) or time difference of arrival (TDOA) of the signals.…”

Section: Introductionmentioning

confidence: 99%

“…However, in the condition of signal non‐line‐of‐sight (NLOS) propagation, such as multipath fading, crowded cities or those with unfavorable weather, the positioning accuracy is not satisfactory 2 . With the popularity of long‐time‐evolution (LTE) signals, by measuring the signal features, large‐scale outdoor positioning with wireless signals becomes possible 3 …”

Section: Introductionmentioning

confidence: 99%

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Outdoor positioning based on deep learning and wireless network fingerprint technology

Lei

Zhang

et al. 2020

Int J RF Microw Comput Aided Eng

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Localization has attracted considerable attention in recent years because of the growth in location-based services and computer-aided technology. In particular, wireless fingerprint-based positioning techniques have received considerable attention due to their satisfactory positioning performance with smartphones. In this work, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. First, long-timeevolution (LTE) mobile networks are used to provide positioning information, and the collected measurements of LTE signals are converted into grayscale fingerprint images to construct the fingerprint database. To overcome the instability of LTE signals, several data enhancement methods are leveraged to increase the diversity of the fingerprint database. Second, a modified deep residual network (MResNet) coarse localizer is used to learn reliable features from the fingerprint image database. Then, inspired by the ideas of feature fusion and transfer learning, a multilayer perceptron (MLP)-based fine localizer is used to further learn the features of the fingerprint images and achieve a better positioning performance. Our experimental results convincingly reveal that the proposed positioning system can achieve satisfactory positioning performance in a variety of outdoor environments.

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Learning Indoor Area Localization: The Trade-Off Between Expressiveness and Reliability

Laska

Blankenbach

2023

Machine Learning for Indoor Localization and Navigation

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Deep Learning for Fingerprint-Based Outdoor Positioning via LTE Networks

Cited by 15 publications

References 26 publications

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Outdoor positioning based on deep learning and wireless network fingerprint technology

Learning Indoor Area Localization: The Trade-Off Between Expressiveness and Reliability

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