采用自适应核函数的空间时频分布盲源分离

Blind Source Separation Using Adaptive Kernal Function Spatial Time Frequency Distribution

基于空间时频分布的盲源分离算法可以用来分离具有不同时频分布的信号.时频分布的交叉项影响着盲分离的性能,而不同的时频分布对交叉项的抑制效果也不同.目前的盲源分离算法多是基于固定核的时频分布,而自适应核函数对交叉项的抑制能力要优于固定核的时频分布.提出采用自适应核函数时频分布的盲源分离算法,这种算法具有比较强的抑制交叉项能力和抗噪声能力.仿真表明,无论是在有噪声还是无噪声的情况下,这种算法的盲分离性能均优于采用Cohen类时频分布的盲源分离算法.

Blind source separation （BSS） based on spatial time frequency distribution can separate signals with different time frequency distributions. However the cross terms in the time frequency distribution affect the BSS performance, and different time frequency distributions have different abilities in reducing cross terms. While most current BSS methods are based on fixed kernel functions, we propose BSS based on adaptive time frequency kernel function to improve the performance of resisting cross terms. The proposed technique has strong ability of removing cross terms and is immune to noise. Computer simulations show that the performance of the proposed algorithm is better than that of those using Cohen class time frequency distributions in environments with or without noise.