Using a simple linear interpolation should be the easiest method to accomplish this. Let's say you have some sample measurement data in vectors named "time" and "data":
%define the starting and ending times of your measurements
start_date = '20100101'; %sstring containing tarting date in yyyyMMdd
end_date = '20100102'; %string containing ending date in yyyyMMdd
start_time = '120000'; %string containing starting time in HHmmSS
end_time = '120000'; %string containing ending time in HHmmSS
%make a time vector for the measurement data
Nsample = 2000;
t1 = datetime(strcat(start_date, start_time), 'InputFormat', 'yyyyMMddHHmmSS');
t2 = datetime(strcat(end_date, end_time), 'InputFormat', 'yyyyMMddHHmmSS');
time = t1:seconds(Nsample):t2;
%make some sample measurement data using a sine wave
data = sin(2*pi*linspace(0,1,length(time)));
Now, let's create a regularly spaced time vector "timeInterp" that you want to interpolate the measured data onto:
%make interpolating time vector
Nsec = 750;
timeInterp = t1:seconds(Nsec):t2;
Note that both the "time" and "timeInterp" vectors contain date strings. These will need to be converted to numerical values before interpolating. This can be done using the "datenum" function:
%convert date string vectors to date numbers
timeNum = datenum(time);
timeInterpNum = datenum(timeInterp);
Now you can use the "fit" function to interpolate the data onto the regularly spaced time vector:
%make a fit object that will linearly inerpolate over the data set
myfit = fit(timeNum(:), data(:), 'linearinterp');
%interpolate the data onto the regularly spaced time vector using the fit object
dataInterp = myfit(timeInterpNum);
Note that the "fit" function is a part of the Curve Fitting Toolbox. You can check the interpolated data against the original data in a plot:
%plot the data
figure;
plot(timeNum, data, 'x', 'markersize',10);
hold on;
plot(timeInterpNum, dataInterp, 'k.');
datetick('x'); %change the x-axis labels to date strings
legend({'Measured data','Interpolated data'});
This approach will be much faster than using "for" and "while" loops. For more information, check the following documentation pages:
