IEICE TRANSACTIONS on Fundamentals
A Generalized Covariance Matrix Taper Model for KA-STAP in Knowledge-Aided Adaptive Radar
Summary :
A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.6 pp.1163-1170
- Publication Date
- 2016/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.1163
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Shengmiao ZHANG
University of Electronic Science and Technology of China
Zishu HE
University of Electronic Science and Technology of China
Jun LI
University of Electronic Science and Technology of China
Huiyong LI
University of Electronic Science and Technology of China
Sen ZHONG
University of Electronic Science and Technology of China
Keyword
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Shengmiao ZHANG, Zishu HE, Jun LI, Huiyong LI, Sen ZHONG, "A Generalized Covariance Matrix Taper Model for KA-STAP in Knowledge-Aided Adaptive Radar" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 6, pp. 1163-1170, June 2016, doi: 10.1587/transfun.E99.A.1163.
Abstract: A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.1163/_p
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@ARTICLE{e99-a_6_1163,
author={Shengmiao ZHANG, Zishu HE, Jun LI, Huiyong LI, Sen ZHONG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Generalized Covariance Matrix Taper Model for KA-STAP in Knowledge-Aided Adaptive Radar},
year={2016},
volume={E99-A},
number={6},
pages={1163-1170},
abstract={A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.},
keywords={},
doi={10.1587/transfun.E99.A.1163},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - A Generalized Covariance Matrix Taper Model for KA-STAP in Knowledge-Aided Adaptive Radar
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1163
EP - 1170
AU - Shengmiao ZHANG
AU - Zishu HE
AU - Jun LI
AU - Huiyong LI
AU - Sen ZHONG
PY - 2016
DO - 10.1587/transfun.E99.A.1163
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2016
AB - A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.
ER -
English
