A self-learning mean optimization filter to improve bluetooth 5.1 AoA indoor positioning accuracy for ship environments

Lin, Qianfeng; Son, Jooyoung; Shin, Hyeongseol

doi:10.1016/j.jksuci.2023.01.019

Cited by 9 publications

(2 citation statements)

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“…Nonetheless, due to the influence of multipath effects, the estimation results of elevation and azimuth are often not precise enough. Thus, in the literature [9], a Mean Optimization Filter (MOF) is proposed to enhance the accuracy of the Bluetooth 5.1 AoA indoor positioning technology. The concept of MOF is to mitigate the impact of noisy data by identifying the optimal average within the dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, due to the continual changes in the surrounding environment, MOF cannot autonomously adjust relevant parameters from the data. Consequently, [10] introduces a Self-Learning Mean Optimization Filter (SLMOF). SLMOF can automatically adjust relevant parameters based on real-time data to ensure the accuracy of the optimal average.…”

Section: Related Workmentioning

confidence: 99%

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A Comparison of Deep Learning Models for IQ Fingerprint Map Based Indoor Positioning in Ship Environments

2024

KSII TIIS

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The importance of indoor positioning has grown in numerous application areas such as emergency response, logistics, and industrial automation. In ships, indoor positioning is also needed to provide services to passengers on board. Due to the complex structure and dynamic nature of ship environments, conventional positioning techniques have limitations in providing accurate positions. Compared to other indoor positioning technologies, Bluetooth 5.1-based indoor positioning technology is highly suitable for ship environments. Bluetooth 5.1 attains centimeter-level positioning accuracy by collecting In-phase and Quadrature (IQ) samples from wireless signals. However, distorted IQ samples can lead to significant errors in the final estimated position. Therefore, we propose an indoor positioning method for ships that utilizes a Deep Neural Network (DNN) combined with IQ fingerprint maps to overcome the challenges associated with accurate location detection within the ship. The results indicate that the accuracy of our proposed method can reach up to 97.76%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%