Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling
Document Type
Article
Date of Original Version
2-1-2002
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 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.
Publication Title, e.g., Journal
IEEE Transactions on Signal Processing
Volume
50
Issue
2
Citation/Publisher Attribution
Saha, Supratim, and Steven M. Kay. "Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling." IEEE Transactions on Signal Processing 50, 2 (2002): 224-230. doi: 10.1109/78.978378.
