# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 1 Introduction  
ADS-B is the next-generation Air Traffic Control (ATC) surveillance system identified by the International Civil Aviation Organization (ICAO) [1]. The space-based ADS-B system places the ADS-B receivers on low-orbiting satellites to overcome the limitations of traditional radar and ground-based ADS-B systems [2]. Each aircraft in the airspace can be monitored and located in the shortest time. Then, the accidents such as MH370 and AF447 can be effectively avoided. However, the number of aircraft covered by a space-based receiver is becoming larger with the development of aviation industry [3]. It is easy for different ADS-B signals reaching the receiver simultaneously and overlapping. Thus, the signal separation methods are essential in the space-based ADS-B system. Depending on the type of antennas required, the ADS-B signal separation methods can be classified into single-antenna based and array-antenna based separation methods. At present, array-antenna based separation algorithms [4; 5; 6] are widely studied. They use the spatial characteristics of ADS-B signals for signal separation. However, if the direction difference of arrival between overlapping signals is less than the array resolution or the receiver only has single antenna, the array-antenna based separation methods will fail. In this case, single-antenna based separation algorithms can be used to separate the overlapped signals. So far, the ADS-B signal separation algorithms based on a single antenna can be divided into three categories. The first approach is to utilize the power difference between overlapping signals. At present, this approach can only separate two signals, and cannot separate the overlapping signals with a small power difference effectively. For example, [PERSON] et al. [7] proposed an accumulation and classification algorithm. The signals are separated based on k-means algorithm by using the power difference. Moreover, thismethod has a high accuracy when the power difference between signals is over 3 dB. [PERSON] et al. [8] proposed a reconstruction and cancellation algorithm. This method fails when the input SNR of overlapped signals are low or the power difference between two signals is large. Furthermore, [PERSON] et al. [9] proposed a time-domain ADS-B blind signal separation (TDBSS) algorithm. Since the amplitude values of the two signals need to be estimated, the method requires a certain relative time delay between the overlapped signals, and it fails when the signals completely overlap. The second approach is to utilize the carrier frequency difference between overlapped signals. This approach can separate multiple overlapping signals, but large carrier frequency difference is needed. For instance, [PERSON] et al. [10] proposed a projection algorithm single antenna (PASA). It converts a single antenna signal into a array antenna signal and uses projection algorithm to separate signals. However, the PASA can only separate two signals with a certain relative time delay and over 240 kHz carrier frequency difference correctly. In order to solve the problem of time delay, [PERSON] et al. [11] proposed an improved PASA. Two signals can be separated as long as their relative time delay is greater than 0.5 \(\upmu\)s, but their carrier frequency difference is no less than 300 kHz. [PERSON] et al. [12] proposed a single-antenna separation method based on empirical mode decomposition (EMD). This method can separate multiple overlapped signals, but the minimum separable carrier frequency difference is 300 kHz and the problem of the mode aliasing needs to be solved. The third approach utilizes other characteristics of ADS-B signal to separate overlapped signals, such as sparse [13] and pulse position modulation (PPM) [14] characteristics. In general, traditional single-antenna based ADS-B signal separation methods are limited by the power difference, carrier frequency difference or relative time delay between overlapped signals. When all three parameters are small, it has difficulty in separating the overlapped signals.