Estimation of chirp signals by MCMC

Author

Lin, Chung-Chieh ; Djuric, P.M.

Author_Institution

Dept. of Electr. & Comput. Eng., State Univ. of New York, Stony Brook, NY, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

265

Abstract

This paper considers the problem of parameter estimation of chirp signals by using the Bayesian methodology. The concept of “mirror points” for constant-amplitude chirp signals is introduced, and its effect on the overall multicomponent chirp parameter estimation performance assessed. By combining the chirpogram with a Markov chain Monte Carlo (MCMC) technique, it is shown that accurate estimates can be obtained for signals comprising many chirps. Simulation results demonstrate that the parameter estimates are in agreement with the CRLB for SNRs as low as 2 dB

Keywords

Bayes methods; Markov processes; Monte Carlo methods; parameter estimation; signal representation; Bayesian methodology; MCMC; Markov chain Monte Carlo technique; chirp signals; chirpogram; constant-amplitude chirp signals; mirror points; multicomponent chirp parameter estimation performance; parameter estimation; Bayesian methods; Chirp; Doppler radar; Frequency estimation; Gaussian noise; Monte Carlo methods; Parameter estimation; Physics; Radar applications; Sonar applications;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

Conference_Location

Istanbul

ISSN

1520-6149

Print_ISBN

0-7803-6293-4

Type

conf

DOI

10.1109/ICASSP.2000.861938

Filename

861938