Hilbert transform of FFT pruned cross correlation function for optimization in time delay estimation

Abstract

Measuring delay using cross correlation technique with signals that are corrupted by noise and are received at two spatially separated sensors often result incorrect measurement. This paper presents a new technique that is Hilbert transform of FFT pruned cross correlation function to improve the accuracy of time delay estimates. To investigate the performance of this technique, frequency algorithms are provided to generate synthetic complex base band signals. For each iteration, it generates two sets of signal. We have to specify input parameters to the simulation model any time delay of signal 2 from signal 1, length and number of complex base band signals to be generated. Such model imitate real-life system, and by varying the uncorrelated complex base band signals to various SNR conditions, further analysis can be performed to compare the performance of this new technique with other existing techniques such as parabolic interpolation, zero crossing of Hilbert transform and FFT pruning.