We will use the Octave or MATLAB tools for calculating coefficients, removing the noise from the data and much more. (But if you would like to read about that let us know in the comments section.) Don’t worry, you don’t have to know all the math to learn how to design digital filters. The math behind that is not straightforward and it will be omitted here. The tricky part of a design process is the calculation of coefficients. The first one for moving through coefficient vector and the second for raw data vector. Easy right? 🙂įrom a programmer’s point of view, the whole process can be done with two nested loops. Finally, y’s are subsequent values of the filter output. Vector filed with a’s contains raw data and vector with c’s contains coefficients. The first four steps of the filtering process are illustrated in the picture below. Length of the sliding window is called filter order. All it comes to a sliding window through raw data and calculating the subsequent output values, so filtering with FIR is similar to the moving average. FIR filterįiltering with an FIR filter is quite easy to understand. If you would like to learn more about other filter design let me know in the comment section. FIR filters are easier to implement so we try to use them first. In general, we distinguish two kinds of digital filters FIR (filters with Finite Impulse Response ) and IIR (recurrence filters with Infinite Impulse Response). You will understand digital filtering and learn how to make your measurements smoother.
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