Often we need to estimate the time difference of arrival between two signals to find the location of a target. Many algorithms gives the time delay corresponding to a sample number or time delay is a function of the sampling frequency. To get away with this problem subsample time delay estimation procedure is used. One can see that if the sampling frequency is very high the subsample may not be necessary. Can anyone help to find when subsample is necessary and when it become irrelevant? I am trying to understand the other associated factor.
Even though the signals are sampled you can get accuracy which is well above the accuracy offered by the samples as long as you sample using Nyquist.
Actually, Using the Matched Filter you can achieve the CRLB (Cramer Rao Lower Bound) for Delay Estimation.
If you caclculate the CRLB for Time Delay Estimation you'll see it depends on the Signal BW and the SNR and not the sampling frequency (Given Nyquist sampling).
How can that be achieved?
You can interpolate the the cross correlation signal and by simple math infer the time delay in resolutions well beyond the sampling resolution.
For instance, with signals with BW of ~ 15 MHz we shall sample by 60 Mhz.
The sampling resolution (Assuming RADAR) is ~ 2.5 [Meter] yet the CRLB at decent SNR will give you a bound of few Centi Meters which can be achieved using the Matched Filter + Interpolation.