Title

A noniterative maximum likelihood parameter estimator of superimposed chirp signals

Document Type

Conference Proceeding

Date of Original Version

9-26-2001

Abstract

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 ad 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.

Publication Title

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volume

5

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