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⛄ 内容介绍
一种最小均方差自适应滤波器及其实现方法.所述最小均方差自适应滤波器充分利用了滤波器的半并行结构,从而实现了乘法器的复用,进而减少了乘法器的使用量;采用延时器的方法解决了滤波器半并行结构导致的抽头系数的存储控制问题,从而使得抽头系数不需存储,而且系数更新也可以实现流水的方式;所述方法可以还可以通过动态地对最小均方差自适应滤波器中使用的抽头个数进行调整,以适应不同抽头数量的需求.
⛄ 部分代码
%--------------------------------------------------------------------------
% One of my friends asked me about the FxLMS algorithm. So, in return,
% I provided him a little example of a single channel feed-forward active
% noise control system based on the FxLMS. You can find many good
% information in "Active Noise Control Systems - Algorithms and DSP
% Implementations," written by S. M. Kuo and D. R. Morgan in 1996.
%
% Here is the sketch of the system.
%
% +-----------+ +
% x(k) ---+--->| P(z) |--yp(k)----------------> sum --+---> e(k)
% | +-----------+ ^- |
% | | |
% | \ ys(k) |
% | +-----------+ +-----------+ | |
% +--->| C(z) |--yw(k)-->| S(z) |---+ |
% | +-----------+ +-----------+ |
% | \ |
% | \----------------\ |
% | \ |
% | +-----------+ +-----------+ |
% +--->| Sh(z) |--xs(k)-->| LMS |<-------+
% +-----------+ +-----------+
%
% I used FIR filter to model P(z), C(z), S(z), and Sh(z).
%
% Imagine that the noise x(k) is propagating from the source to the sensor,
% through the fluid medium P(z). The sensor measures the arriving noise as
% yp(k).
% Report the result
figure
subplot(2,1,1)
plot([1:T], e_cont)
ylabel('Amplitude');
xlabel('Discrete time k');
legend('Noise residue')
subplot(2,1,2)
plot([1:T], Yd)
hold on
plot([1:T], Yd-e_cont, 'r:')
ylabel('Amplitude');
xlabel('Discrete time k');
legend('Noise signal', 'Control signal')
⛄ 运行结果
⛄ 参考文献
[1]刘才勇. 一种最小均方差自适应滤波器及其实现方法:, CN101083462[P].
