Maximum Likelihood Estimation of Co-Channel Multicomponent Polynomial Phase Signals Using Importance Sampling

Unlike some traditional polynomial phase signal (PPS) parameter estimation methods restricted to monocomponent case, this paper focuses on the parameter estimation of multicomponent PPSs mixed in a single channel, which is more sophisticated and always involves the cross-term issue. In this investigation, based on the model of multicomponent PPSs in additional white Gaussian noise, we partition the maximum likelihood estimation into two consecutive steps. The first one involving estimation of polynomial coefficients is intensively studied using importance sampling, while the second one involving the estimation of amplitude and initial phase is trivial. Numerical experiments show satisfactory estimation performance even if the parameters are closely spaced.