FPGA Implementation Of Fixed-point ICA-R Algorithm

Independent component analysis with reference (ICA-R) is a semi-blind source separation algorithm. It incorporates some prior information about the sources into the conventional ICA to extract the desired sources under some measurements. Compared with traditional ICA, ICA-R outputs only signals of interest in a predefined order, provides much improved performance and has fast convergency speed by using a little prior information. It has been efficiently applied to many fields such as speech processing and fMRI (functional magnetic resonance imaging) data analysis.However, the computation complexity of ICA-R discourages its time sensitive application. As such, fixed-point ICA-R algorithm is given to reduce the computation complexity of ICA-R at the arithmetic level. However, the fixed-point ICA-R algorithm still cannot meet the requirement of real-time separation. It is well known that hardware implementation of an algorithm provides an optimal parallelism environment, and thus can provide faster and real-time solutions. After comparison with general-purpose processor (GPP), DSP and ASIC, we chose FPGA to implement fixed-point ICA-R algorithm.The main works of this thesis are as follows. (1) described traditional design technique and DSP-based implementation platform technique, the second technique is used and MATLAB/Simulink and System Generator are accordingly studied. (2) briefly introduced the fixed-point ICA-R algorithm, and classified the algorithm into five modules, which were input module, unmixing module, weight vector updating module, judgment module, and output module, respectively. (3) designed each of five modules based on FPGA. To efficiently implement the nonlinear function in the weight vector updating module, we presented and compared two approaches, one was to construct a look-up table (LUT), the other was to use the piecewise linear approximation. (4) added control blocks and completed the system-level design of the algorithm. (5) carried out extensive experiments with various synthesized sources and their random mixtures to illustrate the efficacy of the FPGA implementation of fixed-point ICA-R algorithm. The synthesized signals include sine wave signal, speech signal and many noise signals. The experiment results show that the hardware design is correct and efficient.