TY - JOUR
T1 - Passive time delay estimation in non-Gaussian noise
AU - Messer, Hagit
AU - Shor, Gadi
AU - Schultheiss, Peter M.
PY - 1999
Y1 - 1999
N2 - This correspondence deals with the structure of the maximum-likelihood (ML) estimator for time delay with arbitrary signal and noise statistics. At high signal-to-noise ratios (SNR's), the ML estimation performs a nonlinear operation on the delayed difference of the two received waveshapes. The required nonlinearity depends only on the noise statistics. At low SNR', a closed-form simple expression for the ML, which depends only on the noise statistics and on the second-order statistics of the signal, is provided. With statistically independent noise processes, the estimator correlates two vectors generated by separate nonlinear operations on the two received waveshapes.
AB - This correspondence deals with the structure of the maximum-likelihood (ML) estimator for time delay with arbitrary signal and noise statistics. At high signal-to-noise ratios (SNR's), the ML estimation performs a nonlinear operation on the delayed difference of the two received waveshapes. The required nonlinearity depends only on the noise statistics. At low SNR', a closed-form simple expression for the ML, which depends only on the noise statistics and on the second-order statistics of the signal, is provided. With statistically independent noise processes, the estimator correlates two vectors generated by separate nonlinear operations on the two received waveshapes.
UR - http://www.scopus.com/inward/record.url?scp=0032689865&partnerID=8YFLogxK
U2 - 10.1109/78.782196
DO - 10.1109/78.782196
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AN - SCOPUS:0032689865
SN - 1053-587X
VL - 47
SP - 2531
EP - 2534
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 9
ER -