Distance Metric Learning for Radio Fingerprinting Localization

Bai, Siqi; Luo, Yong; Yan, Mingjiang; Wan, Qun

doi:10.1016/j.eswa.2020.113747

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…A different and interesting proposal is presented in [54], in which the authors propose two algorithms: large margin nearest neighbors and neighborhood component analysis, to find out the best metric to improve the accuracy of a pedestrian localization system and its adaptability. The basic concept of this study is to select the metric that brings best performance in a KNN based localization system without overloading the system.…”

Section: A Machine Learning In Scene Analysismentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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Section: A Machine Learning In Scene Analysismentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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“…Metric learning is a hot topic in machine learning, which has been utilized in practical applications (Cao et al, 2019 ; Bai et al, 2021 ). Metric learning learns a feature space that is more effective than the original space.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fusion Based Method for Imbalanced Data Classification

et al. 2022

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The imbalance problem is widespread in real-world applications. When training a classifier on the imbalance datasets, the classifier is hard to learn an appropriate decision boundary, which causes unsatisfying classification performance. To deal with the imbalance problem, various ensemble algorithms are proposed. However, conventional ensemble algorithms do not consider exploring an effective feature space to further improve the performance. In addition, they treat the base classifiers equally and ignore the different contributions of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transformation and an adaptive weighted voting scheme. First, we utilize modified metric learning to obtain an effective feature space based on imbalanced data. Next, the base classifiers are assigned different weights adaptively. The experiments on multiple imbalanced datasets, including images and biomedical datasets verify the superiority of our proposed ensemble algorithm.

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“…In this study, we suggested the method of MLE initialization through RF fingerprinting. RF fingerprinting is a localization method that identifies the position based on the received signal characteristics (e.g., RSS and Cell ID), which are "fingerprints" of the signals that are expected to be received at a corresponding location [50]- [55]. The search space of MLE was initialized using RF fingerprinting, and the final position solutions were obtained based on our RF fingerprintingcombined maximum likelihood (ML) problem formulation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of RF Fingerprinting-Aided RSS-Based Target Localization for Emergency Response

Lee,

Kang,

Jeong

et al. 2022

Preprint

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Target localization is essential for emergency dispatching situations. Maximum likelihood estimation (MLE) methods are widely used to estimate the target position based on the received signal strength measurements. However, the performance of MLE solvers is significantly affected by the initialization (i.e., initial guess of the solution or solution search space). To address this, a previous study proposed the semidefinite programming (SDP)-based MLE initialization. However, the performance of the SDP-based initialization technique is largely affected by the shadowing variance and geometric diversity between the target and receivers. In this study, a radio frequency (RF) fingerprinting-based MLE initialization is proposed. Further, a maximum likelihood problem for target localization combining RF fingerprinting is formulated. In the three test environments of open space, urban, and indoor, the proposed RF fingerprinting-aided target localization method showed a performance improvement of up to 63.31% and an average of 39.13%, compared to the MLE algorithm initialized with SDP. Furthermore, unlike the SDP-MLE method, the proposed method was not significantly affected by the poor geometry between the target and receivers in our experiments.

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Distance Metric Learning for Radio Fingerprinting Localization

Cited by 10 publications

References 26 publications

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

Adaptive Fusion Based Method for Imbalanced Data Classification

Evaluation of RF Fingerprinting-Aided RSS-Based Target Localization for Emergency Response

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