Improving indoor geomagnetic field fingerprinting using recurrence plot-based convolutional neural networks

Abid, Mahdi; Lefebvre, Grégoire

doi:10.1080/17489725.2020.1856428

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Riedl et al [19] analyzed the characteristics and application fields of generalized recurrence plots. In general, the nonlinear time-series analysis methods based on the recurrence plot theory have received much attention from researchers in various fields, and they have been successfully applied to many fields, such as geology [20][21][22][23], ecology and biology [24][25][26], neuroscience [27][28][29][30][31], economic dynamics [32][33][34], industrial manufacturing, mechanical damage, and monitoring [25,[35][36][37][38][39][40], medicine [41][42][43][44], image processing, and audio and video analysis [45][46][47]anda CNN-based magnetic fingerprinting system using recurrence plots (RPs) was proposed as sequence fingerprints. Ref.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

2022

Journal of Environmental and Public Health

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The nonlinear time-series analysis method, based on the recurrence plot theory, has received great attention from researchers and has been successfully used in multiple fields. However, traditional recurrence plots that use Heaviside step functions to determine the recursive behavior of a point in the phase space have two problems: (1) Heaviside step functions produce a rigid boundary, resulting in information loss; and (2) the selection of the critical distance, ε , is crucial; if the selection is inappropriate, it will result in a low-dimensional dynamics error, and as of now, there exists no unified method for selecting this parameter. With regard to the problems described above, the novelty of this article lies in the following: (1) when determining the state-phase point recursiveness, a Gaussian function is used to replace the Heaviside function, thereby solving the rigidity and binary value problems of the recursive analysis results caused by the Heaviside step function; and (2) texture analysis is performed on a recurrence plot, new ways of studying complex system dynamics features are proposed, and a system of complex system dynamic-like measurement methods is built.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

2022

Journal of Environmental and Public Health

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“…Abid trained a CNN to transform a Recurrence Plot (RP) and a magnetic map into deep features. His method yielded location classification accuracy improvements of 3.05% and 3.64% [7] compared with another CNN-based system treating fingerprints relying on instantaneous magnetic field data [23]. In the same year, he proposed an improved CNN-based magnetic indoor positioning system using an attention mechanism, which outperformed the initial RP-based CNN but resulted in a much higher level of prediction latency [24].…”

Section: Learning-based Methodsmentioning

confidence: 99%

“…INS is capable of stand-alone positioning, but it can provide only relative results, and its accuracy decreases dramatically (tens of kilometers per hour at most [5]) as the operation time increases [6]. For Wi-Fi and Bluetooth, much effort is required to install and maintain a large amount of infrastructure, which makes the application of these technologies less attractive [7]. The accuracies that can typically be obtained with Wi-Fi and Bluetooth are 1-10 m and 2-15 m, respectively [8].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Localization Method for Vehicles Based on Transformer

Lu,

Wei,

Yuan

2024

Applied Sciences

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Magnetic positioning is a promising technique for vehicles in Global Navigation Satellite System (GNSS)-denied scenarios. Traditional magnetic positioning methods resolve the position coordinates by calculating the similarity between the measured sequence and the sequence generated from the magnetic database with criteria such as the Mean Absolute Difference (MAD), PRODuct correlation (PROD), etc., which usually suffer from a high mismatch rate. To solve this problem, we propose a novel magnetic localization method for vehicles based on Transformer. In this paper, we cast the magnetic localization problem as a regression task, in which a neural network is trained by equidistant sequences to predict the current position. In addition, by adopting Transformer to perform magnetic localization of vehicles for the first time, magnetic features are extracted, and positional relationships are explored to guarantee positioning accuracy. The experimental results show that the proposed method can greatly improve the magnetic positioning accuracy, with an average improvement of approximately 2 m.

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“…2.2). Depending on the further growing field of recurrence analysis and ever new applications as such as in machine learning, novel similarity measures or metrics will be required, such as for comparing field data and spatial patterns, or time series with uncertainties and gaps [161], [e.g. ].…”

Section: Recurrence Definitionsmentioning

confidence: 99%

Trends in recurrence analysis of dynamical systems

Marwan

Kraemer

2023

Eur. Phys. J. Spec. Top.

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The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g. for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research.

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Improving indoor geomagnetic field fingerprinting using recurrence plot-based convolutional neural networks

Cited by 7 publications

References 20 publications

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

Magnetic Localization Method for Vehicles Based on Transformer

Trends in recurrence analysis of dynamical systems

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