基于分块自适应的在线Infomax及其扩展算法

自适应在线Infomax及其扩展算法适用于非平稳环境下的信号分离，具有广泛应用。本文结合批处理分离算法和自适应在线分离算法的优点，提出了分块自适应在线分离算法。并详细推导了基于在线Infomax及其扩展算法的分块自适应更新公式。在此基础上，还推导了一种基于峭度方差调整步长的分块在线变步长公式。仿真结果表明，新算法具有在线算法适用于非平稳环境的优点，在与传统自适应在线算法分离效果相当的条件下，运算量大大降低，数据处理时间大大减少。 

New Block-Adaptive Online Infomax and Extended Infomax Algorithms

Online Infomax and extended Infomax algorithms are applied widely to signal separation at nonstationary circumstance. A new block-adaptive online ICA algorithm is proposed which combines the benefits of batch processing and adaptive online processing. In this paper, we deduce the block adaptive online updating formulae of Infomax and extend Infomax algorithms in detail and introduce a step-size updating regulation which is based on the kurtosis covariance of ouputs. The computer simulations show that the new algorithms can apply to nonstationary circumstance like other online algorithms and the speed of separating signals of the new algorithm is much faster than the old one with the same performance.