Name
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 and 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. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICASSP.2001.940316 | ICASSP |
Keywords | Field | DocType |
separable optimization problem,non-iterative maximization,maximum likelihood,complex amplitude,likelihood function,chirping rate,noniterative maximum likelihood parameter,ml technique,chirp signal,computationally modest implementation,grid search,importance sampling,maximum likelihood estimation,chirp,monte carlo methods,noise,maximum likelihood estimator,vectors,optimization problem,maximization,parameter estimation | Hyperparameter optimization,Importance sampling,Mathematical optimization,Likelihood function,Chirp,Estimation theory,Optimization problem,Mathematics,Maximization,Estimator | Conference |
ISBN | Citations | PageRank |
0-7803-7041-4 | 0 | 0.34 |
References | Authors | |
2 | 2 | |