Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

In order to resolve engineering problems that the performance of traditional blind source separation (BSS) methods deteriorates or even becomes invalid when unknown signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), median (MF), system can achieve suppression in an environment proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function designed based two independence criteria BSS. Meanwhile, combining quantum computation theory slime mould algorithm (SMA), (QSMA) proposed QSMA used solve function. The called (QSMA-BSS). simulation results show QSMA-BSS superior methods. Compared previous methods, has wider applications range, stable performance, higher precision.