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Independently convolutional gated recurrent neural unit for space-based ADS-B signal separation with single antenna
by
Li, Chuankun
, Bi, Yan
in
ADS-B system
/ Aircraft
/ Algorithms
/ Broadcasting antennas
/ Carrier frequencies
/ Deep learning
/ Methods
/ Neural networks
/ Power
/ Separation
/ Signal processing
/ Surveillance
/ System effectiveness
2023
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Independently convolutional gated recurrent neural unit for space-based ADS-B signal separation with single antenna
by
Li, Chuankun
, Bi, Yan
in
ADS-B system
/ Aircraft
/ Algorithms
/ Broadcasting antennas
/ Carrier frequencies
/ Deep learning
/ Methods
/ Neural networks
/ Power
/ Separation
/ Signal processing
/ Surveillance
/ System effectiveness
2023
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Independently convolutional gated recurrent neural unit for space-based ADS-B signal separation with single antenna
by
Li, Chuankun
, Bi, Yan
in
ADS-B system
/ Aircraft
/ Algorithms
/ Broadcasting antennas
/ Carrier frequencies
/ Deep learning
/ Methods
/ Neural networks
/ Power
/ Separation
/ Signal processing
/ Surveillance
/ System effectiveness
2023
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Independently convolutional gated recurrent neural unit for space-based ADS-B signal separation with single antenna
Journal Article
Independently convolutional gated recurrent neural unit for space-based ADS-B signal separation with single antenna
2023
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Overview
Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical technology to transform aircraft navigation by improving safety and overall effectiveness in the aviation industry. However, overlapping of ADS-B signals is a large challenge, especially for space-based ADS-B systems. Existing traditional methods are not effective when dealing with cases that overlapped signals with small difference (such as power difference and carrier frequency difference) require to be separated. In order to generate an effective separation performance of the ADS-B signals by exploring its temporal relationship, Independently Convolutional Gated Recurrent Neural Unit (Ind-CGRU) is presented for encoder–decoder network construction. Experimental results on the dataset SR-ADSB demonstrate that the proposed Ind-CGRU achieves good performance.
Publisher
Springer Nature B.V
Subject
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