BDS B1I multipath channel statistical model comparison between static and dynamic scenarios in dense urban canyon environment

Chen, Xin; He, Di; Pei, Ling

doi:10.1186/s43020-020-00027-7

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…In future work, we plan to further enhance the platform by incorporating additional sensor simulations onto the existing infrastructure. Additionally, we intend to design a multi-sensor fusion experiment [28] in order to evaluate the functionality of the simulation module. At present, the simulation of the sea surface on the platform does not take into account the impact of sea conditions.…”

Section: Discussionmentioning

confidence: 99%

Photorealistic simulation platform for autonomous landing of fixed-wing aircraft

Xue,

Sun,

et al. 2022

JGPS

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To investigate the vision-based autonomous landing of fixed-wing aircraft, we propose a photorealistic simulation platform that leverages ROS, Unreal Engine, and Pixhawk 4. This platform adopts a software-in-the-loop model consisting of rendering, control, communication, and sensor modules. To achieve realistic rendering control, the seawater hydrodynamics and fixed-wing aircraft dynamics are modeled. Based on the platform, we create simulation scenarios under different weather and disturbance conditions for autonomous landing of the aircraft carrier. The platform solves the problem that datasets of related scenes are difficult to collect and experiments are difficult to carry out. To verify the developability of the platform, we design and implement a runway line feature point extraction method (vision-based pose estimation algorithm), and evaluate the performance of the method under various conditions. Experiments show that our software-in-the-loop platform enables vision-based algorithm verification and supports simulation research for the autonomous landing of fixed-wing aircraft.

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Section: Discussionmentioning

confidence: 99%

Photorealistic simulation platform for autonomous landing of fixed-wing aircraft

Xue,

Sun,

et al. 2022

JGPS

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“…Presently, the following technologies are used for time transfer using satellites: all-in-view (AV), common-view (CV), precise point positioning (PPP), two-way satellite time and frequency transfer, and others [5][6][7]. Namely, PPP technology is advantageous for its high precision as well as its simple and sustainable operation in time transfer applications [8,9]; however, due to inherent uncertainty regarding the receiving end of the navigation satellite signal, complex environmental impacts on normal signal reception can occur (such as signal occlusion and cofrequency interference), which in turn affect the continuity and reliability of GNSS time services [10,11]. Simultaneously, a jitter in GNSS time transfer data exists at this stage due to noise, thus further impacting the long-term stability of time transfer [12].…”

Section: Introductionmentioning

confidence: 99%

GPS + 5G fusion for high-precision time transfer

Liu,

Tu,

et al. 2024

Meas. Sci. Technol.

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With the continuous development and increasing popularity of the fifth generation (5G) of mobile communications technology, its fusion with the Global Navigation Satellite System (GNSS) data can effectively improve problems with service interruptions or poor satellite signal reception. In this study, GPS and 5G data were fused, and the resulting experimental algorithm showed that it effective improves time transfer’s frequency stability and reliability. Moreover, this technique reduced the noise level of time-transmitted clock offset sequences, while suppressing short-term mutations. By simulating different degrees of satellite signal occlusion, it was further verified that the GPS+5G fusion method can provide stable, high-precision, and real-time delivery services under insufficient satellite signal reception. This provides a reference for high-precision time transfer technology in complex environments and further improves the reliability of GNSS high-precision time transfer.

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“…In an open environment, BDS can achieve centimeter-level positioning accuracy. Unfortunately, satellite signals are subject to multipath and non-line-of-sight (NLOS) interference, resulting in large positioning errors that often occur suddenly in complex urban environments [ 3 , 4 ]. BDS signals are emitted from satellites, propagate through space and the atmosphere, and finally reach the receiver.…”

Section: Introductionmentioning

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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BDS B1I multipath channel statistical model comparison between static and dynamic scenarios in dense urban canyon environment

Cited by 15 publications

References 18 publications

Photorealistic simulation platform for autonomous landing of fixed-wing aircraft

Photorealistic simulation platform for autonomous landing of fixed-wing aircraft

GPS + 5G fusion for high-precision time transfer

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

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