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Estimatenoise 1.0

  Date Added: August 05, 2013  |  Visits: 242

Estimatenoise

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Some curve fitting or smoothing tools can benefit from knowledge of the noise variance to expect on your data. Kalman filters use this information, also some spline fitting tools. So I wrote a function to extract the noise variance from a signal vector. It also works on any specified dimension of an array.A few examples of this code in use:Simple linear data, with purely additive N(0,1) gaussian noise:t = 0:10000;x = t + randn(size(t));mv = estimatenoise(x)mv = 1.0166Gaussian noise added to a sine wave (Nominal variance = 0.01)t = linspace(0,1,1000)';x = sin(t*50) + randn(size(t))/10;mv = estimatenoise(x)mv = 0.0096887Pure gaussian noise, with a nominal variance of 9. (Note that var would have been a better estimator for this particular case...)mv = estimatenoise(3*randn(2,3,1000),3)mv = 9.6584 8.2696 8.632 9.2404 8.5346 9.7725A piecewise constant function with multiple discontinuities. The true noise variance should be 0.01.t = linspace(0,1,1000);X = round(cos(t*6*pi)) + randn(size(t))/10;plot(t,X)var(X) % var will be wildly in errorans = 0.68256estimatenoise(X)ans = 0.010882Test if estimatenoise is able to recover the variance of a normally distributed random sample with unit variance. (Yes, it will be much slower than var.)mean(estimatenoise(randn(1000,1000)))ans = 1.0002Estimatenoise can now handle non-uniformly spaced series (by request.) In the next example,the actual noise variance was 1.0 here. Perform the operation 1000 times, then look at the median variance estimate. How well did we do?t = sort([randn(1,100) , randn(1,100)+5]);X = repmat(sin(t*5)*100,1000,1) + ... randn(1000,length(t));Estimatenoise is clearly wrong when the spacing is ignored.median(estimatenoise(X,2))ans = 16.438Supplying the sampling "times", we get quite a reasonable result.median(estimatenoise(X,2,t))ans = 1.1307Estimatenoise also works on data with replicates. In this example, each point will be replicated up to 3 times. The actual noise variance was again 1.0 here. I'll also compare the increase in times required for estimatenoise when t is supplied.t = sort([0:1:100, 1:2:100, 1:4:100]);X = repmat(sin(t/10)*100,1000,1)+ ... randn(1000,length(t));Again, estimatenoise is clearly wrong when the non-uniform spacing is ignored.tic,median(estimatenoise(X,2)),tocans = 4.2056Elapsed time is 2.690135 seconds.Supplying the sampling "times", we again get quite a reasonable result. The time penalty is not quite 2x.tic,median(estimatenoise(X,2,t)),tocans = 1.0116Elapsed time is 4.864486 seconds.

Requirements: No special requirements
Platforms: Matlab
Keyword: Actual Distributed Do3ft Estimate Ignoredmedianestimatenoisex Ans Median Operation Perform Randn Randn Lengthtestimatenoise Random Repmatsint Sample Slower Sortrandn Spacing Supplying Times Wrong
Users rating: 0/10

License: Freeware Size: 10 KB
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