By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

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:

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