Independent component analysis (ICA) is an efficient blind source separation technique, but the extracted independent components are randomly permuted in classic ICA algorithms, and subsequent identification is required to find the desired component. Such a two-stage method causes inefficiency. By utilizing the prior information, ICA with reference (ICA-R) algorithm can extract the desired source signal in one stage without subsequent processing. There are many theoretical extensions on ICA-R, but few hardware implementations can be found. Therefore, the efficient VLSI design of a fast one-stage independent component extracting system based on ICA-R algorithm is presented in this paper. The proposed system consists of Preprocess module and Iteration module, which are designed highly parallel and pipelined to accelerate the extraction process. The designed system is implemented in Kintex-7 FPGA, and its performance is verified using synthesized signal. Experiment results show that the presented system can extract the desired components in one stage without subsequent identification, and the highly parallel circuit structure of the system speeds up the component extracting process by 3.8× compared to software implementation.

中文翻译：

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基于ICA-R算法的快速单级独立分量提取系统的VLSI设计

独立成分分析（ICA）是一种有效的盲源分离技术，但提取的独立成分在经典的ICA算法中是随机排列的，需要后续识别才能找到所需的成分。这种两阶段方法导致效率低下。利用先验信息，具有参考的ICA（ICA-R）算法可以在一个阶段提取所需的源信号，而无需进行后续处理。ICA-R 有很多理论上的扩展，但是很少能找到硬件实现。因此，本文提出了一种基于 ICA-R 算法的快速一级独立分量提取系统的高效 VLSI 设计。所提出的系统由预处理模块和迭代模块组成，它们被设计为高度并行和流水线以加速提取过程。所设计的系统在Kintex-7 FPGA中实现，并使用合成信号验证了其性能。实验结果表明，所提出的系统可以在一个阶段提取所需的元件而无需进行后续识别，并且系统的高度并行电路结构通过以下方式加速了元件提取过程3.8×与软件实现相比。