Transfer Learning-Based Outdoor Position Recovery With Cellular Data

Zhang, Yige; Ding, Aaron Yi; Ott, Jörg; Yuan, Mingxuan; Zeng, Jia; Zhang, Kun; Rao, Weixiong

doi:10.1109/tmc.2020.2968899

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…As future work, we continue to explore more advanced machine learning techniques for Telco localization. For example the recent work [42] explored transferable knowledge from training data set to testing data. Such success inspired us to potentially find transferring knowledge between MR samples and GPS locations.…”

Section: Discussionmentioning

confidence: 99%

Context-Aware Telco Outdoor Localization

Zhang

Rao

Yuan

et al. 2022

IEEE Trans. on Mobile Comput.

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Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely RLoc, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work RLoc can greatly improve Telco location accuracy. For example, RLoc on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4% better than state-of-the-art.

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Section: Discussionmentioning

confidence: 99%

Context-Aware Telco Outdoor Localization

Zhang

Rao

Yuan

et al. 2022

IEEE Trans. on Mobile Comput.

Self Cite

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“…For example, in [279] the authors developed an application to collect WiFi signal and sensor data. Mobile service providers could use systems logs available in their networks as in [187], [188]. A number of datasets with annotated RSSI signals are publicly available; Ujiindoorloc is one of the early datasets [358], where annotated RSSI measurements have been recorded in a number of buildings and for a large number of APs.…”

Section: A Used Datasetsmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Burghal,

Ravi,

Rao

et al. 2020

Preprint

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The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising methods for mitigating these problems, as ML (especially deep learning) offers powerful practical data-driven tools that can be integrated into localization systems. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets.A main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. Besides the ML methods, the utilized input features play a major role in shaping the localization solution; we present a detailed discussion of the different features and what could influence them, be it the underlying wireless technology or standards or the preprocessing techniques. A detailed discussion is dedicated to the different ML methods that have been applied to localization problems, discussing the underlying problem and the solution structure. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field.This survey is self-contained, as we provide a concise review of the main ML and wireless propagation concepts, which shall help the researchers in either field navigate through the surveyed solutions, and suggested open problems.

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“…In recent years, visual place recognition has received great attention in the field of machine vision, which can be used to solve the problem of location information localization. If relatively accurate geo-location information is added to these images, they can be of great benefit in areas such as outdoor localization [1], pedestrian detection [2], autonomous driving [3], etc. In addition, pictures with geolocation information can also help environment perception technology for robots [4] and urban construction.…”

Section: Introductionmentioning

confidence: 99%

Geo-Location Method for Images of Damaged Roads

Zhang

Wang

et al. 2022

Electronics

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Due to the large difference between normal conditions and damaged road images, geo-location in damaged areas often fails due to occlusion or damage to buildings and iconic signage in the image. In order to study the influence of post-war building and landmark damage conditions on the geolocation results of localization algorithms, and to improve the geolocation effect of such algorithms under damaged conditions, this paper used informative reference images and key point selection. Aiming at the negative effects of occlusion and landmark building damage in the retrieval process, a retrieval method called reliability- and repeatability-based deep learning feature points is proposed. In order to verify the effectiveness of the above algorithm, this paper constructed a data set consisting of urban, rural and technological parks and other road segments as a training set to generate a database. It consists of 11,896 reference images. Considering the cost of damaged landmarks, an artificially generated method is used to construct images of damaged landmarks with different damage ratios as a test set. Experiments show that the database optimization method can effectively compress the storage capacity of the feature index and can also speed up the positioning speed without affecting the accuracy rate. The proposed image retrieval method optimizes feature points and feature indices to make them reliable against damaged terrain and images. The improved algorithm improved the accuracy of geo-location for damaged roads, and the method based on deep learning has a higher effect on the geo-location of damaged roads than the traditional algorithm. Furthermore, we fully demonstrated the effectiveness of our proposed method by constructing a multi-segment road image dataset.

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Transfer Learning-Based Outdoor Position Recovery With Cellular Data

Cited by 13 publications

References 31 publications

Context-Aware Telco Outdoor Localization

Context-Aware Telco Outdoor Localization

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Geo-Location Method for Images of Damaged Roads

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