Digital multi-beam synthesis technology is generally used in the on-orbit satellite-based Automatic Dependent Surveillance–Broadcast (ADS-B) system. However, the probability of successfully detecting aircraft with uneven surface distribution is low. An adaptive digital beamforming method is proposed to improve the efficiency of aircraft detection probability. The current method has the problem of long operation time and is not suitable for on-orbit operation. Therefore, this paper proposes an adaptive beamforming method for the ADS-B system based on a fully connected neural network (FCNN). The simulation results show that the calculation time of this method is about 2.6 s when more than 15,000 sets of data are inputted, which is 15–80% better than the existing methods. Its detection success probability is 10% higher than those of existing methods, and it has better robustness against large amounts of data.

The satellite-based ADS-B system is an important realization of efficient aviation management in the future. However, the phased array antenna of the satellite payload is prone to multi-channel amplitude-phase mismatch in the variable environment. In this study, the optimal beam distribution is designed and simulated by establishing a signal reception probability detection model. In the signal demodulation part, the LMS-based amplitude-phase mismatch calibration technique is proposed to address the shortcomings of the traditional amplitude-phase mismatch calibration method that needs to be re-calibrated every time the system is turned on and the calibration accuracy is not high. The simulation results show that the method can effectively calibrate the amplitude-phase mismatch phenomenon, and the error is less than the accuracy requirement, which has obvious application advantages over the traditional method.