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

• Published in: IET communications Vol. 13; no. 12; pp. 1787 - 1792
• Main Authors: Li, Chunteng, Jiang, Yuzhong, Liu, Fangjun
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.07.2019
• Subjects: acoustic signal processing; array signal processing; computational complexity; ELF communication; extremely‐low‐frequency communication; improved generalised sidelobe cancellation algorithm; improved generative model; interference cancellation algorithm; interference cancellation field; interference suppression; Research Article; signal‐to‐interference‐plus‐noise ratio gain; speech enhancement; speech enhancement generative adversarial network; traditional improved algorithm; interference suppression; signal-to-interference-plus-noise ratio gain; improved generative model; interference cancellation field; ELF communication; speech enhancement generative adversarial network; improved generalised sidelobe cancellation algorithm; traditional improved algorithm; interference cancellation algorithm; speech enhancement; acoustic signal processing; extremely-low-frequency communication; computational complexity; array signal processing
• ISSN: 1751-8628; 1751-8636
• DOI: 10.1049/iet-com.2018.6217

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.