IMPROVED GENERALIZED CROSS-CORRELATION ALGORITHM FOR TIME DELAY ESTIMATION BASED ON SECONDARY CORRELATION AND DATA SEGMENTATION

摘要

In order to improve the performance of the generalized cross correlation (GCC) algorithm for time delay estimation (TDE) in a low SNR environment and when the signals are non-stationary, an improved GCC algorithm based on secondary correlation and data segmentation (GCCSS) is proposed. First, the received signals are divided into several segments, and two selection criteria based on correlation coefficient and stationarity of signals are provided to select credible data for following calculation. Then the second correlation was used in the algorithm to suppress interference noise. The result of the second correlation is multiplied by a weighting function to get the generalized cross correlation function. Finally, an exponent transform and multiply processing based on segment were proposed to de-noise. This algorithm is verified by some practical data. The experiment results demonstrate the superior TDE performance of GCCSS under a low SNR circumstance and its feasibility and practicality.