ESTIMATING RANDOM AMPLITUDE POLYNOMIAL PHASE SIGNALS - A CYCLOSTATIONARY APPROACH

Modeling of a class of nonstationary signals with randomly time-varying amplitude and parametric polynomial phase is addressed. A novel approach is proposed for the estimation of the time-varying phase by exploiting the higher order cyclostationarity of these signals. The method does not require nonlinear search, is easy to implement, and yields consistent estimates for the parameters. The resulting algorithms are theoretically tolerant to a large class of noises including additive stationary non-Gaussian noise and any Gaussian noise. Simulation examples supporting the theory are provided.