The generation of GPS L1 C/A signals passes several steps including a spread spectrum modulation of the GPS baseband signals. The main benefits of spread spectrum communication, for example, are multiple-access communication, natural and artificial interference resistance, secure communication and others. Spread spectrum communication is a type of communication where the baseband (narrow bandwidth) of transmitted signals are intentionally spread in frequency domain so that the signal has a wider bandwidth than the baseband bandwidth. With the spread spectrum communication, multiple-accesses of the GPS signals can be performed. Global positioning system (GPS), or in general global navigation satellite system (GNSS) is a type of spread spectrum communication.
APPCODE TO MEASURE PULSE MATLAB CODE
The C/A code implementation in MATLAB and C/C++ are presented as well. If you want to understand why there is an abs, or what relevant info you are losing by not representing the phase of the fft, you may want to read a bit more about the DFT transform to understand exactly what you get.In this post, C/A codes for GPS signals generation will be discussed. If by frequency you meant the frequency representation of your signal, then to a first approximation, you just want to plot the abs of the FFT to get an idea of where the energy is: plot(abs(fft)) UPDATE: I realize that I assumed you meant by "frequency" of your signal the pitch or base harmonic or frequency with the most energy, however you want to look at it. 95%, 99%, or some other number would depend on how much noise corrupts your signal. So to account for that noise, you would take the absolute max of the autocorrelation (autocorrelation(length(autocorrelation)/2+1), and then find where the autocorrelation is larger than, say, 95% of that maximum value for the first time in the second half of the signal. Since the signal shifted by a multiple of its period will always look like itself, you need to make sure that the maximum you find indeed corresponds to the period of the signal and not one of its multiples.īecause of the noise in your signal, the absolute maximum could very well occur at a multiple of your period instead of the period itself. (The autocorrelation will be symmetric with its maximum in the middle.) By finding that maximum, you find the first place where the shifted signal looks more or less like itself. The signal frequency will then be: frequency = indexMax * Fs / L Īlternatively, faster and working fairly well too depending on the signal you have, take the autocorrelation of your signal: autocorrelation = xcorr(signal) Īnd find the first maximum occurring after the center point of the autocorrelation. Note: to get from indexMax to the actual frequency of interest, you will need to know the length L of the fft (same as the length of your signal), and the sampling frequency Fs. Where indexMax is the index where the max fft value can be found. Last, if your signal has an offset, as is the case with the one you show, you want to get rid of that offset before taking the fft so that you do not get a max at the origin representing the DC component.Įverything I described put in one line would be: = max(abs(fft(signal-mean(signal)))) The index will correspond to the normalized frequency with maximum energy. Since the fft gives you the frequency representation of the signal, you want to look for the maximum, and since the fft is a complex signal, you will want to take the absolute value first.
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