In measurement and signal processing applications, accurately measuring the frequency of periodic signals is an often encountered task. With the widely known, FFT-based methods, we can only find the solution with limited precision. By increasing the accuracy of the process, we also set higher requirements in terms of complexity and computing resources.
The signal to be processed is not always available entirely, often we have limited lengths of it. This is the case when doing real time signal analysis. The recursive discrete Fourier-transformation (rDFT) is a handy tool which enables us to compute running frequency spectrum resource-friendly.
In frequency analysis tasks, it is important to have some information of the fundamental frequency of the signal, otherwise we can not guarantee that its frequency components will match the DFT-bins' frequencies. The adaptive Fourier-analysator (AFA) proposes a solution for this problem.
In my thesis, I introduce and examine digital signal processing techniques in practice, on computer in Matlab and in advanced, FPGA based embedded systems.