A noniterative maximum likelihood parameter estimator of superimposed chirp signals

We address the problem of parameter estimation of superimposed chirp signals in noise. The approach used here is a computationally modest implementation of a maximum likelihood (ML) technique. The ML technique for estimating the complex amplitudes, chirping rates and frequencies reduces to a separable optimization problem where the chirping rates and frequencies are determined by maximizing a compressed likelihood function which is a function of only the chirping rates and frequencies. Since the compressed likelihood function is multidimensional, its maximization via grid search is impractical. We propose a non-iterative maximization of the compressed likelihood function using importance sampling. Simulation results are presented for a scenario involving closely spaced parameters for the individual signals.