cembeddedsignal-processinggoertzel-algorithm

Implementation of Goertzel algorithm in C


I am implementing BFSK frequency hopping communication system on a DSP processor. It was suggested by some of the forum members to use Goertzel algorithm for the demodulation of frequency hopping at specific frequencies. I have tried implementing the goertzel algorithm in C. the code is follows:

float goertzel(int numSamples,int TARGET_FREQUENCY,int SAMPLING_RATE, float* data)
{
    int     k,i;
    float   floatnumSamples;
    float   omega,sine,cosine,coeff,q0,q1,q2,result,real,imag;

    floatnumSamples = (float) numSamples;
    k = (int) (0.5 + ((floatnumSamples * TARGET_FREQUENCY) / SAMPLING_RATE));
    omega = (2.0 * M_PI * k) / floatnumSamples;
    sine = sin(omega);
    cosine = cos(omega);
    coeff = 2.0 * cosine;
    q0=0;
    q1=0;
    q2=0;

    for(i=0; i<numSamples; i++)
    {
        q0 = coeff * q1 - q2 + data[i];
        q2 = q1;
        q1 = q0;
    }
    real = (q1 - q2 * cosine);
    imag = (q2 * sine);
    result = sqrtf(real*real + imag*imag);
    return result;
}

When I use the function to calculate the result at specific frequencies for a given dataset, I am not getting the correct results. However, if I use the same dataset and calculate the goertzel result using MATLAB goertzel() function, then I get the results perfectly. I am implemented the algorithm using C, with the help of some online tutorials that I found over the internet. I just want to get the view of you guys if the function is implementing the goertzel algorithm correctly.


Solution

  • If you are saying that the Matlab implementation is good because its results match the result for that frequency of a DFT or FFT of your data, then it's probably because the Matlab implementation is normalizing the results by a scaling factor as is done with the FFT.

    Change your code to take this into account and see if it improves your results. Note that I also changed the function and result names to reflect that your goertzel is calculating the magnitude, not the complete complex result, for clarity:

    float goertzel_mag(int numSamples,int TARGET_FREQUENCY,int SAMPLING_RATE, float* data)
    {
        int     k,i;
        float   floatnumSamples;
        float   omega,sine,cosine,coeff,q0,q1,q2,magnitude,real,imag;
    
        float   scalingFactor = numSamples / 2.0;
    
        floatnumSamples = (float) numSamples;
        k = (int) (0.5 + ((floatnumSamples * TARGET_FREQUENCY) / SAMPLING_RATE));
        omega = (2.0 * M_PI * k) / floatnumSamples;
        sine = sin(omega);
        cosine = cos(omega);
        coeff = 2.0 * cosine;
        q0=0;
        q1=0;
        q2=0;
    
        for(i=0; i<numSamples; i++)
        {
            q0 = coeff * q1 - q2 + data[i];
            q2 = q1;
            q1 = q0;
        }
    
        // calculate the real and imaginary results
        // scaling appropriately
        real = (q1 - q2 * cosine) / scalingFactor;
        imag = (q2 * sine) / scalingFactor;
    
        magnitude = sqrtf(real*real + imag*imag);
        return magnitude;
    }