Digital algorithm for maximisation of symmetric ambiguity functions and application to signal time-delay estimation

作     者：Giunta, G 

作者机构：Univ Rome La Sapienza INFO COM Dept I-00184 Rome Italy 

出 版 物：《IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING》 (IEE Proc Vision Image Signal Proc)

年 卷 期：1999年第146卷第6期

页      面：313-316页

核心收录：

主　　题：signal processing optimisation Hilbert filter statistical analysis Hilbert transform Optimisation techniques parameter estimation correlation methods digital algorithm FIR filters zero-crossing point cross-correlation method Filtering methods in signal processing FIR implementation signal time-delay estimation Hilbert transforms random Gaussian signals symmetric ambiguity functions maximisation second-order statistics Other topics in statistics filtering theory Gaussian noise delay estimation reduced Taylor expansion Signal processing theory 

摘      要：Ambiguity functions are usually symmetric around their maximum. In such a case, a consistent estimator of their median value can also detect their maximum. A digital algorithm searches for the zero-crossing point of the Hilbert transform of the estimated function samples. The performance of such a method is analysed by a reduced Taylor expansion, depending on the second-order statistics of the estimated ambiguity samples. The accuracy is explicitly provided in the case of time-delay estimation between random Gaussian signals, corrupted by Gaussian noises. The optimal length of an FIR implementation of the Hilbert filter is also discussed with reference to the generalised cross-correlation method.