基于并行技术和流水线的LMS自适应滤波算法

针对现有自适应滤波算法中数据处理效率低的问题，提出了基于并行技术和流水线的最小均方误差（Least mean square,LMS）自适应滤波算法。该算法构建基于并行技术的多输入多输出滤波器结构，成倍提高系统滤波处理速度；设计基于流水线的LMS自适应滤波权系数求解方法，有效改善了权系数计算效率。最后利用现场可编程门阵列(Field programmable gate array,FPGA)对该算法进行了验证，结果表明，对于四级并行流水线四阶LMS自适应滤波器，其数据处理速率提高了约8倍，在相同的数据处理速率下，其功耗可降低约84%，从而提高了LMS自适应滤波处理速率，降低了系统功耗，实现了高速、超高速数据流的实时自适应滤波处理。

LMS Adaptive Filter Algorithm Based on Parallelism and Pipeline

To increase low efficiency of handling high-speed data in existing adaptive filter algorithms, an least mean squarse(LMS) adaptive filter algorithm based on parallel technology and pipeline is proposed. The proposed algorithm accelerates data processing speed to improve the speed of weight coefficient computing significantly, and reduces the critical path to improve the system working clock effectively. In the experiment based on FPGA, for the LMS adaptive filter based on 4-channel parallel structure and 4-stage pipelines, its data processing rate increases by eight times, and the power consumption can be reduced to 16%, with the same rate of data processing. It can thus realize the real-time LMS adaptive filtering process of high-speed or hyper-speed data stream.