关键词: 欠定盲源分离, 势函数, 多峰值粒子群寻优, 估计混合矩阵, 压缩感知, 信号重构

Abstract:

There are some deficiencies in traditional two-step algorithm for under-determined blind source separation, such as the value of K is difficult to be determined, the algorithm is sensitive to the initial value, noises and singular points are difficult to be excluded, the algorithm is lacking theory basis, etcetera. In order to solve these problems, a new two-step algorithm based on the potential function algorithm and compressive sensing theory was proposed. Firstly, the mixing matrix was estimated by improved potential function algorithm based on multi-peak value particle swarm optimization algorithm, after the sensing matrix was constructed by the estimated mixing matrix, the sensing compressive algorithm based on orthogonal matching pursuit was introduced in the process of under-determined blind source separation to realize the signal reconstruction. The simulation results show that the highest estimation precision of the mixing matrix can reach 99.13%, and all the signal reconstruction interference ratios can be higher than 10dB, which meets the reconstruction accuracy requirements well and confirms the effectiveness of the proposed algorithm. This algorithm is of good universality and high accuracy for under-determined blind source separation of one-dimensional mixing signals.

Key words: Under-determined Blind Source Separation, Potential Function, Multi-peak Value Particle Swarm Optimization, Estimating Mixing Matrix, Compressed Sensing, Signal Reconstruction