# Why adaptive filter does not work in my application

I got a problem when I was trying to denoise a signal. Actually, it is a simple simulation. The signal is the addition of a step signal (The info I wish to get) and a sinusoidal one (the noise I wish to remove). See below However, I tried different parameters of using the adaptive filter, it simply cannot filter out the sinusoidal noise from the step signal. See figure like this. Any suggestions will be greatly appreciated!

Below is the matlab code

clear all
close all

%% walking induced noise
t = [1:5000]*1e-2;
f = 0.1;
WalkNoise = 1*sin(2*pi*f.*t);%+1.5*cos(3*pi*f.*t);
WN = WalkNoise + 0.05*randn(size(WalkNoise));

figure
subplot(3,1,1)
plot(t,WN);
title('Noise');

%% signal
h1 = 14; % height of the signal 1
h2 = 18; % height of the signal 2
L = 5000;  % total length of the signal
bp =2500;  % location of break point
x1 = h1*ones(1,bp);
x2 = h2*ones(1,L-bp);
Sig = [x1,x2];
Sig = Sig + 0.1*randn(size(Sig));

subplot(3,1,2)
plot(t,Sig);
title('Signal');

%% walking-induced-noise + signal
NoisySig = Sig + WN;
subplot(3,1,3)
plot(t,NoisySig); hold on
title('Signal + noise');

figure
plot(t,NoisySig); hold on
title('Signal + noise');

mu = 0.001;            % LMS step size.
[y,e] = filter(ha,WN,NoisySig);
plot(t,e,'r');

• Adaptive filters are used to find s certain state/velocity/acceleration or mapping of such signals - you may refer to them as trackers. In your case you just need to use a simple band pass to filter out the sinusoid.
– Moti
May 7 '15 at 7:00
• You are plotting the error output of the filter (e). What do you get if you plot the output of the filter (y?) I'd try it myself, but Octave doesn't seem to have function matching the Matlab adaptfilt.lms
– JRE
May 7 '15 at 9:14
• Dear Moti: The reason why I prefer adaptive filter it is because noise can change its frequency distribution, although in this case it is fixed. Thanks for the kind help! May 8 '15 at 4:55
• Dear JRE: I may not fully understand your question. Here I wish to use the adaptive filter to remove the noise. So one input of the adaptive filter will be the signal+noise (NoisySig), and another input would be the measured noise. The output of the Adaptive filter is the noise which tries to mimic the real noise. So it is e that I wish to have (e represent the final signal I wish to obtain). May 8 '15 at 4:59