3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning

Cheng, Shuyan; Wang, Shujun; Guan, Wenbai; Xu, He; Li, Peng

doi:10.3390/s20092731

Cited by 31 publications

(20 citation statements)

References 20 publications

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“…Among the 3D localization schemes, some schemes are fully 3D [3,11,18,19], some schemes only indicate the floor and the 2D location of the target object [12,20].…”

Section: D Localziationmentioning

confidence: 99%

“…Recently, the location-based service (LBS) for internet of things (IoT) becomes popular in many fields such as office building, shopping mall, and community building. Since the GPS system cannot catch satellite signals in an indoor environment, many indoor localization systems based on different RF signals have been presented, such as RFID [1][2][3], Bluetooth, and LoRa [4] based localization systems.…”

Section: Introductionmentioning

confidence: 99%

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A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

Chen

Hsu

Chung

2022

Sensors

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Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.

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“…Among the 3D localization schemes, some schemes are fully 3D [3,11,18,19], some schemes only indicate the floor and the 2D location of the target object [12,20].…”

Section: D Localziationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

Chen

Hsu

Chung

2022

Sensors

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“…The best result shows an improvement on accuracy by 75% when compared to the pedestrian localization system without the use of ML. In [36], the authors use a CNN to estimate the relative vertical positioning of a pedestrian, and provide a three-dimensional pedestrian localization with an accuracy of up to 94%, without requiring antenna rotation like other approaches. The authors in [45] use RNN to estimate the orientation and velocity of the pedestrian, and estimate the movement model to track the pedestrian.…”

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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“…Cheng et al [12] proposed a novel 3D localization technique that depends upon DL method: intriguing RFID accurate position with comparative position, analyzing the variant features of RSSI, additional mining data features using DL, and apply the technique to the intelligent library scene. The simulation result shows that this technique has a high position accuracy and optimal scheme reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of deer hunting optimization algorithm for accurate 3D indoor node localization

Durgaprasadarao

Siddaiah

2021

Evol. Intel.

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Indoor localization is one of the emergent technologies in location based services, which find useful for commercial as well as civilian industries. Global position systems are a familiar solution for outdoor localization systems. But the presence of complicated obstacles in buildings poses a major challenge in indoor localization. Though few indoor localization techniques based on ranging and fingerprint based techniques are devised, they are time consuming and laborious. Therefore, this paper devises an efficient deer hunting optimization algorithm with weighted least square estimation (DHOA-WLSE) technique for accurate 3D indoor node localization technique. The proposed DHOA-WLSE technique has the ability to accomplish minimal localization error with least localization time. In DHOA-WLSE technique, the DHOA is used to estimate the primary target location to eliminate the non-line of sight errors. Based on the primary locations attained, the WLSE technique is applied to determine the accurate target's final location. In order to validate the 3D indoor localization performance of the DHOA-WLSE technique, an extensive simulation analysis is performed and the results are investigated in terms of different measures. The simulation outcomes demonstrated the superior performance of the DHOA-WLSE technique over the recent state of art techniques.

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3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning

Cited by 31 publications

References 20 publications

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

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

Design of deer hunting optimization algorithm for accurate 3D indoor node localization

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