Subspace-Based Algorithm for Parameter Estimation of Polynomial Phase Signals

In this correspondence, parameter estimation of a polynomial phase signal (PPS) in additive white Gaussian noise is addressed. Assuming that the order of the PPS is at least 3, the basic idea is first to separate its phase parameters into two sets by a novel signal transformation procedure, and then the multiple signal classification (MUSIC) method is utilized for joint estimating the phase parameters with second-order and above. In doing so, the parameter search dimension is reduced by a half as compared to the maximum likelihood and nonlinear least squares approaches. In particular, the problem of cubic phase signal estimation is studied in detail and its simplification for a chirp signal is given. The effectiveness of the proposed approach is also demonstrated by comparing with several conventional techniques via computer simulations.