Detection and classification of spectrally equivalent processes: a parametric approach

Author

Coulon, Martial ; Tourneret, Jean-Yves ; Ghogho, Mounir

Author_Institution

Nat. Polytech. Inst. of Toulouse, France

fYear

1997

fDate

21-23 Jul 1997

Firstpage

410

Lastpage

414

Abstract

The detection of two spectrally equivalent (SE) processes is addressed. The two SE processes are modeled using two SE parametric models: the noisy AR model and the ARMA model. Higher-order statistics are shown to be an efficient tool for the SE process detection problem. A new detector based on the higher-order Yule-Walker matrix singularity is studied. The detector performance is compared in supervised and unsupervised learning. The model order mismatch is then studied

Keywords

autoregressive moving average processes; autoregressive processes; higher order statistics; matrix algebra; noise; pattern classification; signal detection; spectral analysis; unsupervised learning; ARMA model; SE processes; classification; detection; higher-order Yule-Walker matrix singularity; higher-order statistics; noisy AR model; order mismatch; parametric approach; spectrally equivalent processes; supervised learning; unsupervised learning; Detectors; Higher order statistics; Military communication; Parametric statistics; Signal detection; Signal processing; Spread spectrum communication; Supervised learning; Testing; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Higher-Order Statistics, 1997., Proceedings of the IEEE Signal Processing Workshop on

Conference_Location

Banff, Alta.

Print_ISBN

0-8186-8005-9

Type

conf

DOI

10.1109/HOST.1997.613557

Filename

613557