System and method for determining signal-to-noise ratio (SNR), signal-to-noise and distortion ratio (SINAD) and total harmonic distortion (THD)

1. A system for determining signal-to-noise ratio (SNR), signal-to-noise and distortion ratio (SINAD), and total harmonic distortion (THD) of a signal from which sampled data is obtained, the system comprising:

means for estimating a frequency of a dominant sinusoidal signal from the sampled data;

means for iteratively fitting a curve to the sampled data using the estimated frequency of the dominant sinusoidal signal, the curve comprising a fundamental sinusoid, harmonic components and a DC offset; and

means for computing SNR, SINAD and THD of the signal represented by the sampled data using the fundamental sinusoid, harmonic components and a DC offset from the iteratively fitted curve.

2. The system of claim 1 , wherein the means for iteratively fitting the curve to the sampled data initially assumes that the frequency of the dominant sinusoidal signal is within an initial range of values including the estimated frequency of the dominant sinusoidal signal.

3. The system of claim 2 , in which the range of values is divided into discrete points.

4. The system of claim 3 , in which an error value is plotted for each discrete point.

5. The system of claim 4 , in which a frequency having a minimum error is selected as an updated estimate of the dominant sinusoidal frequency.

6. The system of claim 5 , wherein the means for iteratively fitting the curve to the sampled data subsequently assumes that the frequency of the dominant sinusoidal signal is within an updated range of values including the updated estimated frequency of the dominant sinusoidal signal, the updated range of values being smaller than the initial range of values.

7. The system of claim 6 , further comprising means for determining an amplitude and a frequency of the harmonic components.

8. The system of claim 1 , wherein the means for estimating the frequency of the dominant sinusoidal signal from the sampled data, the means for iteratively fitting the curve to the sampled data, and the means for computing SNR, SINAD and THD of the signal represented by the sampled data comprise a processor and a memory configured to store software instructions to cause the processor to execute algorithms for estimating the frequency of the dominant sinusoidal signal from the sampled data, for iteratively fitting the curve to the sampled data, and for computing SNR, SINAD and THD of the signal represented by the sampled data.

9. The system of claim 1 , wherein the means for iteratively fitting a curve to the sampled data stops iterating when one or more convergence criteria are satisfied.

10. The system of claim 9 , wherein the convergence criteria comprises at least one of the computed SNR, the THD and the SINAD changes by less than a threshold amount from one iteration to a next iteration.

11. A method, implemented in one of a computing device and a processor, for determining signal-to-noise ratio (SNR), signal-to-noise and distortion ratio (SINAD), and total harmonic distortion (THD) of a signal from which sampled data is obtained, the method comprising:

estimating a frequency of a dominant sinusoidal signal from the sampled data;

iteratively fitting a curve to the sampled data using the estimated frequency of the dominant sinusoidal signal, the curve comprising a fundamental sinusoid, harmonic components and a DC offset; and

computing SNR, SINAD and THD of the signal represented by the sampled data using the fundamental sinusoid, harmonic components and a DC offset from the iteratively fitted curve.

12. The method of claim 11 , wherein iteratively fitting the curve to the sampled data includes initially assuming that the frequency of the dominant sinusoidal signal is within an initial range of values including the estimated frequency of the dominant sinusoidal signal.

13. The method of claim 12 , further comprising dividing the range of values into discrete points.

14. The method of claim 13 , further comprising plotting an error value for each discrete point.

15. The method of claim 14 , further comprising selecting as an updated estimate of the dominant sinusoidal frequency a frequency having a minimum error.

16. The method of claim 15 , wherein iteratively fitting the curve to the sampled data includes subsequently assuming that the frequency of the dominant sinusoidal signal is within an updated range of values including the updated estimated frequency of the dominant sinusoidal signal, the updated range of values being smaller than the initial range of values.

17. The method of claim 13 , further comprising determining an amplitude and a frequency of the harmonic components.

18. The method of claim 11 , wherein the signal comprises an analog signal and the method further comprising sampling the analog signal to obtain the sampled data.

19. The method of claim 11 , wherein estimating the frequency of the dominant sinusoidal signal from the sampled data includes:

transforming the sampled data into a frequency domain;

finding a frequency in the frequency domain wherein the transformed sampled data has a largest amplitude; and

estimating the frequency of the dominant sinusoidal signal to be the frequency in the frequency domain wherein the transformed sampled data has the largest amplitude.

20. The method of claim 11 , further comprising stopping iterating when one or more convergence criteria are satisfied.

21. The method of claim 20 , wherein the convergence criteria comprises at least one of the computed SNR, the THD and the SINAD changes by less than a threshold amount from one iteration to a next iteration.