Nonlinear Regression-Based GNSS Multipath Dynamic Map Construction and Its Application in Deep Urban Areas

Lee, Yongjun; Wang, Pai; Park, Byungwoon

doi:10.1109/tits.2023.3246493

Cited by 15 publications

(2 citation statements)

References 29 publications

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“…Several methods have been extensively explored for positioning in multipath and NLOS environments using sensors such as light laser detection and ranging (LiDAR), sky-pointing cameras, and inertial measurement units (IMU) in combination with GNSS. In addition, recent studies [ 20 , 21 , 22 , 23 ] have utilized dynamic users at multiple locations on the road to generate multipath maps and employed residual-based algorithms to address location inaccuracies in urban canyon environments. Nagivation applications integrating a cubature Kalman filter (CKF) method and strap-down inertial navigation system (SINS) have been proposed to improve the navigation error under harsh GNSS environments [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Qin,

Li,

Xie

et al. 2024

Sensors

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The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model’s robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

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Section: Related Workmentioning

confidence: 99%

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Qin,

Li,

Xie

et al. 2024

Sensors

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“…The subsequence of this obstruction is an increased satellite dilution of precision (DOP), which directly degrades the accuracy of user positioning and leads to extensive error propagation along the window orientation. Furthermore, the limited visibility in a single direction frequently fails to meet the minimum number of satellites required for positioning [4], [5], [6]. Consequently, relying solely on GNSS satellite signals in these circumstances presents a significant challenge, demanding supplementary measurements or techniques to enhance the positioning process.…”

mentioning

confidence: 99%

A GNSS/Barometric Altimeter Tightly Coupled Integration for Three-Dimensional Semi-Indoor Mapping With Android Smartphones

Yun,

Park

2024

IEEE Geosci. Remote Sensing Lett.

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This letter introduces a GNSS/altimeter tightly coupled (TC) integration algorithm that combines barometric altitude measurements with GNSS observations to determine 3-D positions. By transforming barometric altitude measurements into Earth-centered Earth-fixed (ECEF) coordinates, a novel TC integration equation is derived. This equation enables accurate positioning with only three GNSS satellites and one altitude measurement, overcoming the limitations of conventional GNSS positioning techniques. A semi-indoor experiment for various smartphones was conducted to validate the proposed method, comparing it against conventional positioning techniques and the National Marine Electronics Association (NMEA) location provided by smartphone chipsets. The results demonstrated that the GNSS/BARO TC integration significantly reduced errors and improved accuracy. Notably, the mean error of 4.74 m horizontally and 2.43 m vertically improved the NMEA results by 63.31% and 89.18%, respectively, even under challenging indoor conditions. The proposed method met FCC-required accuracy levels, making it suitable for enhancing the accuracy and availability of wireless 911 calls, aligning with indoor positioning mandates.

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Extraction and analysis of short-term variation characteristics of code multipath error

Li,

et al. 2024

Advances in Space Research

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Nonlinear Regression-Based GNSS Multipath Dynamic Map Construction and Its Application in Deep Urban Areas

Cited by 15 publications

References 29 publications

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

A GNSS/Barometric Altimeter Tightly Coupled Integration for Three-Dimensional Semi-Indoor Mapping With Android Smartphones

Extraction and analysis of short-term variation characteristics of code multipath error

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