Interference cancellation algorithm based on improved generative model combined with improved generalised sidelobe cancellation in ELF communication

Abstract

To improve the communication quality of extremely-low-frequency (ELF) communication effectively, an interference cancellation algorithm based on the generative model widely used in the artificial intelligence field is proposed. Some magnetic antennas with higher sensitivity and analogue circuits with lower noise floor are designed to suppress various out-of-band interferences. For the first time, a generative model is introduced into the interference cancellation field in ELF communication. Based on the speech enhancement generative adversarial network widely used in speech signal enhancement, an improved generative model that can be applied in the interference cancellation field is proposed to provide some more revelant reference information. By the combination of the improved generative model and the improved generalised sidelobe cancellation (GSC) algorithm, the estimated accuracy of noise and interference is improved effectively. To verify the effectiveness of the proposed algorithm, an experimental platform is built in a laboratory environment, and multiple sets of controlled experiments are performed. Also, the experimental results show that the improved generative model has better performance, better robustness, and relatively lower computational complexity. In addition, compared with the original GSC algorithm and its traditional improved algorithm, the proposed algorithm further promoted the signal-to-interference-plus-noise ratio gain within the range of signal bandwidth.