Single antenna ADS-B overlapping signals separation based on deep learning

As a new type of surveillance technology, Automatic Dependent Surveillance-Broadcast (ADS-B) has been regarded as the cornerstone of the next generation air surveillance. However, a potential problem is that multiple ADS-B signals may overlap when they come from different aircraft simultaneously. This paper explores the possibility of using deep learning to separate pulsed ADS-B overlapping signals. Most existing deep learning algorithms separate the overlapping signals by utilizing the difference in time-frequency domains. But all ADS-B signals are transmitted at the same frequency, those deep learning methods based on time-frequency analysis are ineffective. This paper separates pulsed ADS-B overlapping signals directly in the time domain. Firstly, an encoder is used to extract the mixed features of ADS-B overlapping signals in the time domain. Then, Temporal Convolutional Network (TCN) is used to extract the non-overlapping ADS-B signal feature from the output of encoder. Finally, the separated time-domain signals are reconstructed by a decoder. Simulation results show that the separation accuracy of the algorithm based on deep learning is higher than that of the traditional algorithm.