一种混叠采样条件下的自适应LMS方法

为了降低自适应宽带信号处理中对采样率的要求,提出了一种在混叠采样条件下的自适应LMS算法。该方法不计较信号的带宽,通过设置特定的接收端中频频率与采样率倍数关系,利用混叠的基带信号计算自适应算法迭代运算的误差,从而完成自适应学习过程。通过理论推导和仿真实验,该混叠采样方法与理想采样方法相比收敛稳态误差完全一致,且采样率与信号带宽无关,降低了系统对采样率即模数转换器件的要求,降低了系统复杂度和硬件成本。

An Adaptive LMS Method under Condition of Aliasing Sampling

A novel adaptive LMS method based on aliasing sampling is proposed to reduce the requirement on the sampling rate of adaptive signal processing.The proposed method is irrelevant to signal bandwidth,with specific relationship between intermediate frequency and sampling rate,the adaptive learning process is completed by calculating the iteration error based on the aliasing baseband signals.Theoretical derivation and simulation experiments indicate that compared with the ideal sampling method,the convergence steadystate error of the hybrid sampling method is completely consistent,and the sampling rate is independent of the signal bandwidth,which reduces the requirements of the system on the sampling rate,that is,the analogto-digital conversion device,and also reduces the system complexity and hardware cost.