Satellite coverage traffic volume prediction using a new surrogate model

Zhao, Yong; Wang, Ning; Chen, Quan; Yu, Sunquan; Chen, Xiaoqian

doi:10.1016/j.actaastro.2022.01.026

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…Finally, a software receiver is used to decode the separated signals and retrieve ADS-B information. To reduce the overlap probability of ADS-B signals, many companies employ multi-antenna designs on space-based ADS-B receivers (Blomenhofer et al., 2012; Bettray et al., 2013; Yu et al., 2020; Zhao et al., 2022). Therefore, the proposed algorithm can be directly applied to space-based ADS-B receivers.…”

Section: Discussionmentioning

confidence: 99%

A high-precision and efficient algorithm for space-based ADS-B signal separation

Jia

2023

J. Navigation

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Space-based automatic dependent surveillance-broadcast (ADS-B) receivers can cover thousands of aircraft, each transmitting 6 ⋅ 2 signals per second. As a result, ADS-B signals are very prone to overlap. When the number of aircraft covered by a receiver reaches 3,000, about 90 % of the signals will be overlapping. Overlapped signals can reduce the decoding accuracy of receivers, so that aircraft information cannot be accurately transmitted to the air traffic control (ATC) surveillance system, hence threatening aviation flight safety. It is necessary to propose signal separation algorithms for space-based ADS-B systems. An orthogonal projection linear constrained minimum variance (OPLCMV) algorithm is proposed, which can separate two signals simultaneously based on the linearly constrained minimum variance algorithm by exploiting the characteristics of overlapped signals. Compared with the state-of-the-art extended projection algorithm and the fast independent component analysis algorithm, the OPLCMV method has a higher decoding accuracy for multiple overlapping signals with a small direction difference of arrival or frequency shift. Moreover, the OPLCMV algorithm has a low computational complexity when the number of overlapped signal sources is less than seven.

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Section: Discussionmentioning

confidence: 99%

A high-precision and efficient algorithm for space-based ADS-B signal separation

Jia

2023

J. Navigation

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“…It is vital and large challenge to separate the mixed space-based ADS-B signals. In order to solve this problem, many methods Caisheng et al (2019); Petrochilos et al (2009); Wang et al (2019); Zhao et al (2022) based on array-antenna have been proposed for ADS-B signal separation. For example, Project Algorithm (PA) Petrochilos et al (2009) is used to separate multiple secondary surveillance radar signals.…”

Section: Introductionmentioning

confidence: 99%

Independently Convolutional Gated Recurrent Neural Unit for Space based ADS-B Signal Separation with Single Antenna

2023

Preprint

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Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical technology to transform aircraft navigate through improving safety, efficiency, and overall effectiveness in the aviation industry. However, ADS-B signal overlapping is a large challenge, especially for space-based ADS-B system. The existing traditional methods cannot effectively separate overlapping signals with small difference such as power difference, carrier frequency difference. To solve this problem, we propose Independently Convolutional Gated Recurrent Neural Unit (Ind-CGRU) to build the encoder-decoder network. And Ind-CGRU explores the more temporal relationship of ADS-B signal to generate effective separation mask and improves the ADS-B signal separation performance. Experimental results on SRADSB dataset, demonstrate that the proposed Ind-CGRU achieves the good performance.

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A hybrid deep learning framework for unsteady periodic flow field reconstruction based on frequency and residual learning

Peng

Chen

et al. 2023

Aerospace Science and Technology

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Satellite coverage traffic volume prediction using a new surrogate model

Cited by 9 publications

References 24 publications

A high-precision and efficient algorithm for space-based ADS-B signal separation

A high-precision and efficient algorithm for space-based ADS-B signal separation

Independently Convolutional Gated Recurrent Neural Unit for Space based ADS-B Signal Separation with Single Antenna

A hybrid deep learning framework for unsteady periodic flow field reconstruction based on frequency and residual learning

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