IEICE TRANSACTIONS on Electronics
Target Identification from Multi-Aspect High Range-Resolution Radar Signatures Using a Hidden Markov Model
Summary :
Identification of targets using sequential high range-resolution (HRR) radar signatures is studied. Classifiers are designed by using hidden Markov models (HMMs) to characterize the sequential information in multi-aspect HRR signatures. The higher-order moments together with the target dimension and the number of dominant wavefronts are used as features of the transient HRR waveforms. Classification results are presented for the ten-target MSTAR data set. The example results show that good classification performance and robustness are obtained, although the target features used here are very simple and compact compared with the complex HRR signatures.
- Publication
- IEICE TRANSACTIONS on Electronics Vol.E87-C No.10 pp.1706-1714
- Publication Date
- 2004/10/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Electromagnetic Theory
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Masahiko NISHIMOTO, Xuejun LIAO, Lawrence CARIN, "Target Identification from Multi-Aspect High Range-Resolution Radar Signatures Using a Hidden Markov Model" in IEICE TRANSACTIONS on Electronics,
vol. E87-C, no. 10, pp. 1706-1714, October 2004, doi: .
Abstract: Identification of targets using sequential high range-resolution (HRR) radar signatures is studied. Classifiers are designed by using hidden Markov models (HMMs) to characterize the sequential information in multi-aspect HRR signatures. The higher-order moments together with the target dimension and the number of dominant wavefronts are used as features of the transient HRR waveforms. Classification results are presented for the ten-target MSTAR data set. The example results show that good classification performance and robustness are obtained, although the target features used here are very simple and compact compared with the complex HRR signatures.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/e87-c_10_1706/_p
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@ARTICLE{e87-c_10_1706,
author={Masahiko NISHIMOTO, Xuejun LIAO, Lawrence CARIN, },
journal={IEICE TRANSACTIONS on Electronics},
title={Target Identification from Multi-Aspect High Range-Resolution Radar Signatures Using a Hidden Markov Model},
year={2004},
volume={E87-C},
number={10},
pages={1706-1714},
abstract={Identification of targets using sequential high range-resolution (HRR) radar signatures is studied. Classifiers are designed by using hidden Markov models (HMMs) to characterize the sequential information in multi-aspect HRR signatures. The higher-order moments together with the target dimension and the number of dominant wavefronts are used as features of the transient HRR waveforms. Classification results are presented for the ten-target MSTAR data set. The example results show that good classification performance and robustness are obtained, although the target features used here are very simple and compact compared with the complex HRR signatures.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Target Identification from Multi-Aspect High Range-Resolution Radar Signatures Using a Hidden Markov Model
T2 - IEICE TRANSACTIONS on Electronics
SP - 1706
EP - 1714
AU - Masahiko NISHIMOTO
AU - Xuejun LIAO
AU - Lawrence CARIN
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E87-C
IS - 10
JA - IEICE TRANSACTIONS on Electronics
Y1 - October 2004
AB - Identification of targets using sequential high range-resolution (HRR) radar signatures is studied. Classifiers are designed by using hidden Markov models (HMMs) to characterize the sequential information in multi-aspect HRR signatures. The higher-order moments together with the target dimension and the number of dominant wavefronts are used as features of the transient HRR waveforms. Classification results are presented for the ten-target MSTAR data set. The example results show that good classification performance and robustness are obtained, although the target features used here are very simple and compact compared with the complex HRR signatures.
ER -
English
