Deep Neural Network for Indoor Positioning Based on Channel Impulse Response

Dao, Van-Lan; Salman, Shaik Mohammed

doi:10.1109/etfa52439.2022.9921735

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Additionally, researchers have developed a deep CNN to predict the position of a moving robot. One of the input parameters of this network is a measurement of the channel's impulse response (Dao & Salman., 2022). Furthermore, DNN was suggested for application in the location positioning process within an indoor office environment by the 3rd generation partnership project (3GPP) (Oh & Kim, 2021).…”

Section: Discussion and Analysis Of Deep Learning Algorithmmentioning

confidence: 99%

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A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

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The term “indoor navigation system” pertains to a technological or practical approach that facilitates the navigation and orientation of individuals within indoor settings, such as museums, airports, shopping malls, or buildings. Over several years, significant advancements have been made in indoor navigation. Numerous studies have been conducted on the issue. However, a fair evaluation and comparison of indoor navigation algorithms have not been discussed further. This paper presents a comprehensive review of collective algorithms developed for indoor navigation. The in-depth analysis of these articles concentrates on both advantages and disadvantages, as well as the different types of algorithms used in each article. A systematic literature review (SLR) methodology guided our article-finding, vetting, and grading processes. Finally, we narrowed the pool down to 75 articles using SLR. We organized them into several groups according to their topics. In these quick analyses, we pull out the most important concepts, article types, rating criteria, and the positives and negatives of each piece. Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system.

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Section: Discussion and Analysis Of Deep Learning Algorithmmentioning

confidence: 99%

“…DNN DNN works better than other machine-learning algorithms Deep neural network for indoor positioning based on channel impulse response (Dao & Salman, 2022)…”

mentioning

confidence: 99%

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

Polo-Rodríguez,

Valera,

Peral

et al. 2024

Multimed Tools Appl

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The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m$$ ^2 $$ 2 and 100 m$$ ^2 $$ 2 ) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings.

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An End-to-End Deep Learning Model based on Channel Impulse Response Measurement for 2D Indoor Positioning

Utama,

Umam,

Jang

2024

2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)

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Deep Neural Network for Indoor Positioning Based on Channel Impulse Response

Cited by 3 publications

References 37 publications

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

An End-to-End Deep Learning Model based on Channel Impulse Response Measurement for 2D Indoor Positioning

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