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how to robustly estimate low and up envelope of signal with trend, few level constant steps and noise

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Michal Kvasnicka
Michal Kvasnicka on 19 Sep 2018
Commented: Michal Kvasnicka on 19 Sep 2018
I am looking for robust estimation method of low and up envelope of the signal consisting from smooth trend component, constant steps between few fixed levels and additive noise (+ outliers of course). This question raised during my current research work on real life signal processing.
Typical example of signal is produced by following script:
%%signal definition
% number of samples
Ns = 10000;
% sampling period [secs]
Ts = 1;
time = (1:Ns)*Ts;
% trend component
a = 2;
b = 0;
T = 1e4;
slope = time/Ts * 0.0005;
trend = a * sin(2*pi*time/T) + b + slope;
% steps component (4 constant levels)
step = [zeros(1,1000),linspace(0,1,6),1*ones(1,500),linspace(1,0,6),zeros(1,500),linspace(0,-2,10), -2*ones(1,1200),linspace(-2,1,15),1*ones(1,3000),linspace(1,0,6), zeros(1,3000),linspace(0,-1,6), -1*ones(1,751)];
% noise component (normal noise)
noise = 0.2*randn(1,Ns);
% noise = 0.5*(rand(1,Ns)-0.5);
%%show signals component
close all
hold on
plot(time,trend - 2, 'b--')
plot(time,trend + 1, 'k--')
legend('trend','trend+steps','lowenvelope', 'upenvelope','trend+steps+noise')
title('smooth trend signal with constant steps between 4 levels and noise')
xlabel('time [sec]')
ylabel('value [-]')
hold off
See the following image
The separate signal components are unknown! Steps are always constant and between small number of fixed levels (typically < 4 or 5), so the estimated envelopes should be parallel to the trend signal. Noise is approximated by normal distribution with sigma ~0.1
Any idea how to solve this surprisingly difficult problem? Any relevant references or matlab code links?

  1 Comment

Michal Kvasnicka
Michal Kvasnicka on 19 Sep 2018
In principle I am looking for any robust estimation of trend signal component which is nearly parallel to real trend signal.

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