I'm trying to self learn the art of signal processing whilst moving through my third year pure maths degree.
Sorry if my terminology is incorrect however I hope I am understandable!
I am looking at data which is coming from an accelerometer, distance data from a separate sensor and time data incrementing by 0.01 seconds e.g. to be clear in case my terminology is incorrect I have a dataset which has a row for each 0.01 seconds with the row having data from the accelerometer and distance sensors. I believe this means the data is sampled at 100 Hz.
Please can someone confirm that my choice of using a digital filter is correct?
My reasoning is that the data is not analogue and is digital and as such I should not use a standard Butterworth (or other) filter and should look for a digital version. Is this reasoning correct?
I want to use the data to compare the second derivative with respect to time of the distance with the RSS of acceleration and before I do this I want to 'clean' up the data as much as possible and it is my understanding that filtering will give me this.
I am using Octave to perform the maths and have various pieces of code to filter the data, however I do not feel like I understand the filter settings I should use! Before I start to try and understand the filter settings are all my assumptions and reasoning reasonable?
My Octave code for my filter is as follows:
%I use Octave, however I believe Matlab will be very similar if not identical % I believe that my sample frequency is 100 Hz. mysamf = 100; % Nyquist frequency. I believe this is set to half the sample frequency Fnyq = mysamf/2; % I do not understand the cut-off frequency, however I set it to 45 % I can see through plotting the results that it does make an impact. mycutf=45; % Here I create a 1st order Butterworth filter, using the above restrictions. [b,a]=butter(2, mycutf/Fnyq); % I pass my dataset that contains the displacements. output=filter(b,a,v_dist);
I realize I should have explained this at the beginning however I didn't think that the source of the data would influence the filtering approach (digital/analogue).
My Sensor data is coming from a Pogo stick - the acceleometer mounted at the base of the stick and a displacement sensor measuring the movement of the stick and spring assembly.
The Pogo stick is being used by a Gazelle on steroids so is going wild, however is always bouncing around on and off axis, big jumps small hops, soft ground hard ground.