Detection and Estimation of Multi- Component Polynomial Phase Signals by Constructing Regular Cross Terms

A regular cross terms algorithm is derived for the parameter estimation of the multi-component polynomial phase signals in additive white Gaussian noise. The basic idea is first to separate its phase parameters into two sets by nonlinear proceduresand then each set has half of the parameters in its auto-terms. Furthermore, using two linear transforms to deal with the two signals respectively, the phase coefficients of cross terms can be regulated for the identification and elimination of false peaks caused by the cross terms. Simulations are presented to illustrate the performance of the proposed algorithm.