Best way to filter a Matrix
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I have the postion, velocity and acceleration data from a subject walking. Each variable is a matrix with each column representing a step and each row representing a measurement. However, there are some bad values where VZ didn't record a position (ie 1; 2; 3; 0; 5) or the vaue is way off(ie 1; 2; 3; 1256; 5). I would like to create vectors for average step values for position, velocity and acceleration.
Whats the best way to create the vectors without the bad values?
I am having trouble with the finding the bad values and making the dimensions of the columns agree. Any help is greatly appreciated... Thanks.
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Paulo Silva
am 29 Jun. 2011
Maybe this can help you start with a smooth algorithm
po=[1 2 3 10 4 5 100 6 7 8 9 0 10 11 12 0 13 14 15 16]
p=po;
v=diff(p);
vp=find(abs(v)>2);
vp=vp(1:2:end);
p(vp+1)=(p(vp+2)+p(vp))/2;
t=1:numel(p);
plot(t,po,t,p)
legend('Measurements','Filtered measurements')
p
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Paulo Silva
am 5 Jul. 2011
I just remember something that might be useful for the same purpose, it's the rate limiter from simulink, http://www.mathworks.com/help/toolbox/simulink/slref/ratelimiter.html , you can use the documentation formulas on your problem.
Weitere Antworten (2)
Sean de Wolski
am 29 Jun. 2011
Do you have the curve fitting toolbox? If so, look at:
doc smooth
specifically the 'rlowess' option.
Are all three signals recorded or do you just have acceleration or just position? Assuming you only have one of the signals, do the smoothing before any differentiation or integration.
Andrei Bobrov
am 30 Jun. 2011
my variant
pf = [po(1)-1 po po(end)+1];
p = median(pf(bsxfun(@plus,1:3,(0:length(pf)-3)')),2);
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