A noniterative maximum likelihood parameter estimator of superimposed chirp signals
Date of Original Version
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.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
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.