SPOTTER: A novel asynchronous and independent WiFi and BLE fusion method based on particle filter for indoor positioning

Azaddel, Mohammad Hadi; Nourian, Mohmmad Amin; ShahHosseini, Komeil; Junoh, Suhardi Azliy; Akbari, Ahmad

doi:10.1016/j.iot.2023.100967

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…Bluetooth [4] and Zig Bee [5] are often used for this purpose. Fusion between two technologies, particularly the fusion of Wi-Fi and Bluetooth Low Energy (BLE) [6], have been explored in recent years for better performing indoor positioning system. A prevalent solution for the indoor localization problem is to estimate the position of users or devices through received signal strength (RSS) measurements [7] which are used alongside a position estimation algorithm, such as fingerprint-based algorithm and geometrical calculation.…”

Section: Introductionmentioning

confidence: 99%

Robust Access Points Selection Strategies for Dynamic Indoor Localization

Mazlan,

2024

IEEE Access

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The proliferation of mobile devices has fueled the demand for indoor location-based services. Consequently, a plethora of techniques have emerged to facilitate object and device localization in indoor environments. Among these, fingerprint-based indoor localization systems, which leverage machine learning, stand out as a promising solution for providing accurate localization. Nonetheless, their performance is inherently reliant on the accuracy of the underlying database, and any changes in the indoor layout can significantly impact the wireless signals, subsequently affecting the localization accuracy. To circumvent this issue, this work proposes a novel access point (AP) selection framework to enhance the robustness of fingerprint-based indoor positioning systems in dynamic indoor environments. More specifically, a hybrid Wi-Fi and BLE fingerprint database is constructed by collecting received signal strength (RSS) from the predefined reference points (RPs). To ensure that the fingerprint database remains relevant over time, some RPs are designated as known points so that the system can periodically collect new RSS at the known points. Subsequently, the proposed scheme computes the differences between the RSS of the database and the updated RSS from the new layout to account for the changes occurred. The building-based, floor-based, and zone-based implementation modes determine which APs are reliable to be utilized during localization based on the RSS discrepancies observed in building, floor, and zone, respectively. Results demonstrate that the proposed building-based, floor-based, and zone-based AP selection schemes could achieve reduction in positioning error up to 28.86%, 33.53%, and 39.66%, respectively, compared to the baseline technique without AP selection schemes.

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Section: Introductionmentioning

confidence: 99%

Robust Access Points Selection Strategies for Dynamic Indoor Localization

Mazlan,

2024

IEEE Access

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Liang-Bi Chen . including Wi-Fi and Bluetooth low energy (BLE), for indoor LBS, given the widespread deployment of wireless devices and RF sensors in indoor environments [3].…”

Section: Introductionmentioning

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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Adaptive Weighted Access Point Technique for Robust Dynamic Indoor Positioning

Mazlan,

2024

IEEE Access

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SPOTTER: A novel asynchronous and independent WiFi and BLE fusion method based on particle filter for indoor positioning

Cited by 7 publications

References 33 publications

Robust Access Points Selection Strategies for Dynamic Indoor Localization

Robust Access Points Selection Strategies for Dynamic Indoor Localization

Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

Adaptive Weighted Access Point Technique for Robust Dynamic Indoor Positioning

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