Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
Source abstract
Abstract
Automatic Dependent Surveillance-Broadcast (ADS-B) signals are very vital in air traffic control. However, the space-based ADS-B signals are easily overlapped and their message cannot be correctly received. It is challenge to separate overlapped signals especially for a single antenna. The existing methods have a low decoding accuracy for small power difference, carrier frequency difference and relative time delay between overlapped signals. In order to solve these problems, we apply the deep learning method to single antenna ADS-B signal separation. A multi-scale Conv-TasNet (MConv-TasNet) is proposed to capture long temporal information of the ADS-B signal. In MConv-TasNet, a multi-scale convolutional separation (MCS) network is proposed to fuse different scale temporal features extracted from overlapping ADS-B signals and generate an effective separation mask to separate signals. Moreover, a large dataset is created by using the real ADS-B data. In addition, the proposed method has been evaluated on the dataset. The average decoding accuracy on the test set is 90.34%. It has achieved the state-of-the-art results.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Wireless Signal Modulation Classification · Radar Systems and Signal Processing · Speech and Audio Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
State Key Laboratory of Dynamic Testing Technology and School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
Funding
the National Natural Science Foundation of China · Fundamental Research Program of Shanxi Province
References (22)
Show source-provided references
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