In this paper we present a synchronization technique, for applications using repeated or periodically excited measurements. The problem with existing techniques is their limitations to specific signal and noise conditions, such as white Gaussian noise or narrowband signals. The proposed method extracts statistical information about the underlying signal and noise in the measurements to obtain good synchronization (asymptotically optimal). The Cramér-Rao lower bound (CRLB) is derived for the synchronization problem, including bounds for the underlying signal waveform and the covariance of the noise. The method, which is the maximum-likelihood estimator for both white and colored Gaussian noise, is compared with standard sub-sample estimation and aligning techniques using Monte Carlo simulations. The results show significant improvements compared to standard synchronization techniques.
展开▼
