Time-varying autoregressive modeling of HRR radar signatures

Author

Eom, Kie B.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., Washington, DC, USA

Volume

35

Issue

3

fYear

1999

fDate

7/1/1999 12:00:00 AM

Firstpage

974

Lastpage

988

Abstract

A time-varying autoregressive (TVAR) model is used for the modeling and classification of high range resolution (HRR) radar signatures. In this approach, the TVAR coefficients are expanded by a low-order discrete Fourier transform (DFT). A least-squares (LS) estimator of the TVAR model parameters is presented, and the maximum likelihood (ML) approach for determining the model order is also presented. The validity of the TVAR modeling approach is demonstrated by comparing with other approaches in estimating time-varying spectra of synthetic signals. The estimated TVAR model parameters are also used as features in classifying HRR radar signatures with a neural network. In the experiment with two sets of noncooperating target identification (NCTI) data, about 93% of samples are correctly classified

Keywords

autoregressive processes; discrete Fourier transforms; feature extraction; least squares approximations; maximum likelihood estimation; modelling; neural nets; radar resolution; radar target recognition; signal classification; signal representation; time series; time-frequency analysis; PCA; automatic target recognition; feature extraction; feature space reduction; high range resolution radar signatures; least-squares estimator; low-order discrete Fourier transform; maximum likelihood approach; model order; neural network classification; noncooperating target identification data; parameter estimation; radar signatures classification; time-frequency distribution; time-varying autoregressive model; Application software; Autoregressive processes; Discrete Fourier transforms; Feature extraction; Maximum likelihood estimation; Millimeter wave radar; Polynomials; Radar applications; Radar clutter; Radar signal processing;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.784067

Filename

784067