An ambiguity-reduced phase unwrapping method and its applications in parameter estimation

Abstract

Over the past decades, many phase unwrapping algorithms have been developed and formulated for overcoming the problem of phase ambiguity. In this paper, a novel ambiguity reduced phase unwrapping method is proposed, which unwraps the linear combined phases instead of the instantaneous phases, and thus substantially reduces the degrees of ambiguity. It turns out that the ambiguity-reduce phase unwrapping could be transformed to an optimization problem. Moreover, the proposed unwrapping method has been applied to the estimation of the phase parameter of a single-tone in additive Gaussian white noise. Computer simulations show better capability of our proposed phase unwrapping approach with the traditional unwrapping algorithm, that is, the signal-to-noise ratio (SNR) thresholds of the proposed unwrapping method is much lower than that of traditional algorithms. Meanwhile, the corresponding phase estimator, which is based on the ambiguity-reduced phase unwrapping method, also outperforms the phase estimators tested, achieving a performance that is consistently close to the Cramer-Rao Bound (CRB) for all values of SNR.