Special Issue on “Recent Advances in Indoor Localization Systems and Technologies”

Simon, Gyula; Sujbert, László

doi:10.3390/app11094191

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Technology for estimating the location of workers in indoor environments has been studied for accident prevention and convenience at construction and industrial sites. Furthermore, indoor localization systems are being developed for the realization of the fourth industrial revolution [1][2][3]. For example, if a worker attempts to enter a dangerous area, an indoor localization system can prevent accidents by estimating the location of the worker and warning them.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

2021

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In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

2021

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Ultra-wideband Received Signal Strength Analysis for UAV Indoor NLOS Navigation

Vidan,

Ivănescu,

Grigorie

2023

2023 Power Quality and Electromagnetic Compatibility at Low Frequency (PQEMC-LF)

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A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

Yaro

Maly

Pražák

2023

Sensors

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A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system’s localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user’s instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers’ suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

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Special Issue on “Recent Advances in Indoor Localization Systems and Technologies”

Cited by 3 publications

References 39 publications

Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

Ultra-wideband Received Signal Strength Analysis for UAV Indoor NLOS Navigation

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

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