An Indoor Wi-Fi Localization Algorithm Using Ranging Model Constructed With Transformed RSSI and BP Neural Network

Lin, Yiruo; Yu, Kegen; Hao, Lianxiao; Jing, Wang; Bu, Jinwei

doi:10.1109/tcomm.2022.3145408

Cited by 29 publications

(4 citation statements)

References 46 publications

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“…In [63], the virtual ratio is calculated using the principle of trilateral positioning, on the basis of the RSSI path loss function, narrows the positioning range to set the virtual AP position, and adaptively modifies the inputs of the CNN model in order to improve the positioning accuracy. In [64], a ranging model based on the BP neural network was proposed. This model firstly scales and transforms the collected RSSI by using Equation (18).…”

Section: Integration With Image-based Methodsmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization.

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Section: Integration With Image-based Methodsmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

View full text Add to dashboard Cite

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“…3) SDMR-BPNN Based Fine Estimation: On account of the limited payload of satellite receiver, we further resolve the fine CFO estimation problem with the aid of BPNN, which has the advantages of low complexity and fast model training [17]. According to the clustering results, the PDP samples in each of categories are used to train a BPNN.…”

Section: B Proposed Clustering-nn Based Cfo Estimation Modelmentioning

confidence: 99%

Clustering-NN-Based CFO Estimation Using Random Access Preambles for 5G Non-Terrestrial Networks

Zhen,

Cheng,

Chu

et al. 2024

IEEE Wireless Commun. Lett.

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Non-terrestrial networks (NTNs) are expected to play a pivotal role in the future wireless ecosystem. Due to its high-dynamic characteristics, the accurate estimation and compensation of carrier frequency offset (CFO) are crucial for supporting 5G new radio (NR) enabled satellite direct access. With emphasis on ensuring reliable uplink synchronization, we propose a clustering-neural network based CFO estimation scheme by virtue of NR random access preambles. By leveraging the sparsity and regularity of input samples, the proposed scheme can achieve fast and precise prediction of CFOs, while establishing robustness against time uncertainty and channel variation within a satellite beam. Simulation results validate the feasibility of our scheme in various NTN scenarios, and demonstrate its superiority in terms of stable estimation performance over the existing schemes.

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“…In this study, we applied a widely adopted WKNN algorithm to evaluate the similarity between RSS vectors [53], [54]. In the online phase, the user's location is estimated by evaluating the similarity between the recently acquired fingerprint and the fingerprints recorded in the database.…”

Section: B Location Estimation With Weighted K-nearest Neighborsmentioning

confidence: 99%

Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

Junoh,

Pyun

2024

IEEE Access

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Location estimation in indoor environments using radiofrequency (RF) has garnered considerable attention in recent years owing to the widespread adoption of mobile devices. RF-based fingerprinting-a direct approach that allows location estimation based on observed signals-relies on manual surveys during the offline phase to create a radio map with coordinates and RF measurements at multiple locations. The accuracy of RF fingerprint-based localization is proportional to the number of reference points. However, conventional site survey procedures incur substantial expenses. To alleviate the workload of site surveys and address the challenge of incomplete fingerprint databases, we propose a data-augmentation method to complement existing fingerprint data. Our approach leverages a conditional generative adversarial network with long short-term memory (CGAN-LSTM) prediction model to effectively learn the intricate patterns inherent in the initial training data and generate high-quality synthetic data that align with the underlying data distribution. In an experimental evaluation conducted on a real testbed, our data augmentation framework increased the average localization accuracy by 15.74% compared with fingerprinting without data augmentation. Compared with linear interpolation, inverse distance weighting, and Gaussian process regression, the proposed approach demonstrates an average accuracy improvement ranging from 1.84% to 14.04%, achieving average accuracies of 1.065 and 1.956 m in both scenarios. In experiments conducted in two typical indoor environments using sparse data, the proposed approach substantially reduced localization error and proved comparable to state-of-the-art data-augmentation methods. INDEX TERMSBluetooth low energy (BLE), fingerprint, data augmentation, generative adversarial network (GAN), location estimation. SUHARDI AZLIY JUNOH (Graduate Student Member, IEEE) received the B.Eng. degree in electronics (telecommunications) from Multimedia University, Malaysia, and the master's degree in electrical engineering from Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia. He is currently pursuing the Ph.D. degree in information and communication engineering with Chosun University, South Korea. He was with Infineon Technologies as an Engineer. His current research interests include indoor positioning and navigation, the Internet of Things, mobile computing, and wireless communication systems. JAE-YOUNG PYUN (Member, IEEE) received the B.S. and M.

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An Indoor Wi-Fi Localization Algorithm Using Ranging Model Constructed With Transformed RSSI and BP Neural Network

Cited by 29 publications

References 46 publications

Research Progress of Wireless Positioning Methods Based on RSSI

Research Progress of Wireless Positioning Methods Based on RSSI

Clustering-NN-Based CFO Estimation Using Random Access Preambles for 5G Non-Terrestrial Networks

Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

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