Computationally efficient iterative refinement techniques for polynomial phase signals

Recursive and efficient estimation of polynomial-phase is considered here, with alternatives to the standard Gauss-Newton approach presented. We consider approximations of the likelihood and phase noise distribution to derive recursive approximate maximum likelihood and Bayesian estimators. Monte Carlo simulations indicate that these methods compare favourably with the Gauss-Newton scheme both in terms of computational expense and efficiency thresholds.