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, e.g., Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume
5
Citation/Publisher Attribution
Saha, S., and S. Kay. "A noniterative maximum likelihood parameter estimator of superimposed chirp signals." ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 5, (2001): 3109-3112. doi: 10.1109/ICASSP.2001.940316.