Deep learning methods for fingerprint-based indoor positioning: a review

Alhomayani, Fahad; Mahoor, Mohammad H.

doi:10.1080/17489725.2020.1817582

Cited by 62 publications

(34 citation statements)

References 145 publications

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“…where the mouth (4) is the RSSI value at distance d. e function between the distance d and the value P l (d) of RSSI can be determined by curve fitting to determine the values of parameters P l (d) and n p . However, the signal strength eigenvalue RSSI signal is unstable in the actual environment and thus has large fluctuations, so it is less resistant to noise [14]. Its positioning principle is that the distance from the receiver to the transmitter is determined using the known RSSI value, and then the actual physical coordinates of the receiver are calculated by the trilateral geometric positioning algorithm.…”

Section: Design Of Low-power Indoor Positioningmentioning

confidence: 99%

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Zhang

Huai

2021

Complexity

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In this article, we use a low-power iBeacon network to conduct an in-depth analysis and research on the principle of indoor positioning and adopt an efficient and fast positioning algorithm. Secondly, based on the obtained analysis, an iBeacon-based indoor positioning system is proposed to analyze how to use iBeacon for accurate positioning and whether it can effectively compensate for the current mainstream positioning system. We analyze the requirements of the iBeacon-based indoor positioning system and propose the design of this positioning system. We analyze the platform and environment for software development, design the general framework of the positioning system, and analyze the logical structure of the whole system, the structure of data flow, and the communication protocols between each module of the positioning system. Then, we analyze the functions of the server module and the client module of the system, implement the functions of each module separately, and debug the functions of each module separately after each module is implemented. The feasibility of the algorithm and the performance improvement are confirmed by the experimental data. Our results show that the communication distance is improved by approximately 20.25% and the accuracy is improved by 5.62% compared to other existing results.

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Section: Design Of Low-power Indoor Positioningmentioning

confidence: 99%

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Zhang

Huai

2021

Complexity

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“…Therefore, various indoor positioning systems were developed to provide LBSs even in indoor areas. Depending on the type of wireless communication, wireless communication-based indoor positioning systems can be broadly divided into fingerprint and time-of-flight (TOF) methods [ 6 , 7 ]. In the former, a user’s location is identified based on the received signal strength indicator (RSSI) [ 8 , 9 ] or channel-state information (CSI) [ 10 , 11 ] depending on the relative distance between the transmitter and the receiver.…”

Section: Introduction Of Indoor Positioning System Methodsmentioning

confidence: 99%

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Seong

Lee

Kim

et al. 2021

Sensors

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Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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“…The authors in [5] present a specific review of the application of DL in localization systems that are based on fingerprinting, describing the use of DL according to the raw data used to create the fingerprint, identifying the benefits and highlighting the improvement on its performance, mainly by handling large amounts of data to obtain an accurate estimate of the location of a pedestrian. The authors in [6] focus on how DL is applied in SLAM.…”

Section: B Existing Surveys On Machine Learning and Localizationmentioning

confidence: 99%

“…Due to the growing interest in the application of ML in localization systems, several studies have examined the use of ML in localization. However, these works tend to limit their scope to only one type of technology (radio frequency [3], [4]), or to exclusively only one type of localization technique (e.g., scene analysis [5], simultaneous localization and mapping (SLAM) [6], device-free localization [7]), or to only one type of ML techniques (e.g., deep learning (DL), [5], [6], [8]). In general, works that present the use of ML in localization systems do so from the point of view of ML techniques and their practice, not from the point of view of localization systems and the benefit that they obtain [9].…”

Section: Introductionmentioning

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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Deep learning methods for fingerprint-based indoor positioning: a review

Cited by 62 publications

References 145 publications

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

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

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