Paper
10 June 1996 Noncooperative target classification using hierarchical modeling of high-range resolution radar signatures
Kie Bum Eom
Author Affiliations +
Abstract
The classification of high range resolution radar returns using multiscale features is considered. Because of the characteristics unique to radar signals, such as clutter and sensitivity to viewing angle change, classifiers using features extracted from a single scale do not meet the requirements of non-cooperative target identification (NCTI). We present a hierarchical ARMA model for modeling high range resolution radar signals in multiple scales and apply it to NCTI database containing 5000 test samples and 5000 training samples. We first show that the radar signal at a course scale follows an ARMA process if it follows an ARMA model at a finer scale. The model parameters at different scales are easily computed from the parameters at another scale. Therefore, the hierarchical model allows us to compute spectral features at the coarse scale without adding much computational burden. The multiscale spectral features at five scales are computed using the hierarchical modeling approach, and are classified by a minimum distance classifier. The multiscale classifier is applied to both poorly aligned data and better aligned data. For both data sets, about 95 percent of the radar returns were correctly classified, showing that the multiscale classifier is robust to misalignment.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kie Bum Eom "Noncooperative target classification using hierarchical modeling of high-range resolution radar signatures", Proc. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, (10 June 1996); https://doi.org/10.1117/12.242048
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Cited by 1 scholarly publication.
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KEYWORDS
Radar

Autoregressive models

Optical filters

Data modeling

Doppler effect

Feature extraction

Electronic filtering

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