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Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling

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2 Author(s)
Saha, S. ; Dept. of Electr. & Comput. Eng., Rhode Island Univ., Kingston, RI, USA ; Kay, S.M.

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 that is a function of only the chirping rates and frequencies. Since the compressed likelihood function is multidimensional, its maximization via a grid search is impractical. We propose a noniterative maximization of the compressed likelihood function using importance sampling. Simulation results are presented for a scenario involving closely spaced parameters for the individual signals

Published in:

Signal Processing, IEEE Transactions on  (Volume:50 ,  Issue: 2 )

Date of Publication:

Feb 2002

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