Application of conditional averaging to time delay estimation of random signals

Open access

Abstract

The article presents the possibilities of using the function of conditional average value of a delayed signal (CAV) and the function of conditional average value of a delayed signal absolute value (CAAV) to determine the time delay estimation (TDE) of random signals. For discrete CAV and CAAV estimators, the standard uncertainties of the estimation of function values at extreme points and the standard uncertainties of the TDE were given and compared with the corresponding uncertainties for the direct discrete cross-correlation function (CCF) estimator. It was found that the standard uncertainty of TDE for CAV is lower than for CCF independent of signal-to-noise ratio (SNR) for parameter values of α ≥ 2 and M/N ≥ 0.25 (where: α - relative threshold value, M/N - quotient of number of averaging and number of samples). The standard uncertainty of TDE for CAAV will be lower than for CCF for SNR values greater than 0.35 (for N/M = 1).

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Measurement Science Review

The Journal of Institute of Measurement Science of Slovak Academy of Sciences

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IMPACT FACTOR 2017: 1.345
5-year IMPACT FACTOR: 1.253



CiteScore 2017: 1.61

SCImago Journal Rank (SJR) 2017: 0.441
Source Normalized Impact per Paper (SNIP) 2017: 0.936

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