Signal detection and estimation using atomic decomposition and information-theoretic criteria

An algorithm to detect and estimate a linear mixture of signals corrupted by white Gaussian noise is presented. The number of signals is assumed to be unknown. The algorithm is based on a type of information theoretic criterion, capable of adjusting the probability of false alarm, and uses atomic de...

Full description

Saved in:
Bibliographic Details
Main Authors: Lopez-Risueno, G., Grajal, J., Yeste-Ojeda, O.A.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Array signal processing
Atomic measurements
Chirp modulation
Detection, estimation, filtering, equalization, prediction
Dictionaries
Exact sciences and technology
Gaussian noise
Information theory
Information, signal and communications theory
Maximum likelihood detection
Maximum likelihood estimation
Radar
Signal and communications theory
Signal detection
Signal processing
Signal, noise
Telecommunications and information theory
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An algorithm to detect and estimate a linear mixture of signals corrupted by white Gaussian noise is presented. The number of signals is assumed to be unknown. The algorithm is based on a type of information theoretic criterion, capable of adjusting the probability of false alarm, and uses atomic decomposition refined by the expectation maximization method to efficiently compute the maximum likelihood estimate of the signal parameters. Signals are modeled as chirplets. The algorithm consistency and efficiency are shown by simulation.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2004.1326453