一种改进性能自适应ICA算法研究

Performance Research of Gradient-Based Optimum Block Adaptive ICA Algorithm In Blind Source Separation

研究高动态环境必须实时自适应追踪混合矩阵的变化,快速浮点独立成分分析(Fast-ICA)算法,可以达到快速收敛。依据梯度在线学习算法性能更好,但是它的缺点是收敛慢,并且依赖适当的收敛算子的选择。为解决上述问题,提出了一种依据梯度优化块自适应ICA算法(GBOBA/ICA),包含Fast-ICA和依据梯度在线学习两种算法的优点。进行仿真,结果表明,算法在时间变化的信道中(即混合矩阵快速变化时)有良好的性能。

The fast fixed-point independent component analysis（ICA） algorithm has been widely used in various applications because of its fast convergence and superior performance.However,in a highly dynamic environment,real-time adaptation is necessary for tracking the variations of the mixing matrix.In this scenario,the gradient-based online learning algorithm performs better,but its convergence is slow,and depends on a proper choice of convergence factor.This paper develops a gradient-based optimum block adaptive ICA algorithm（GBOBA/ICA） that combines the advantages of the two algorithms.Simulation results for telecommunication applications indicate that the resulting performance is superior under time-varying conditions.