基于omap的盲分离实现

摘要

This project introduces a FastICA algorithm based on maximum of negative entropy,which searches the direction according to the maximum of negative entropy and this project achieve to extract independent source.We increase the speed to realize the algorithm on OMAP through several methods including rationally allocating hardware resources and optimizing software code.The result shows that this is a very valuable blind source separation method.%本项目介绍了一种基于负熵最大的FastICA算法，它以负熵最大作为一个搜寻方向，实现顺序地提取独立源。并且通过合理分配硬件资源，优化软件代码，提高了该算法在omap上的实现速度。结果表明，这是一种非常有价值的盲源分离新方法。