IEICE TRANSACTIONS on Fundamentals
Persymmetric Structured Covariance Matrix Estimation Based on Whitening for Airborne STAP
Summary :
The estimation problem of structured clutter covariance matrix (CCM) in space-time adaptive processing (STAP) for airborne radar systems is studied in this letter. By employing the prior knowledge and the persymmetric covariance structure, a new estimation algorithm is proposed based on the whitening ability of the covariance matrix. The proposed algorithm is robust to prior knowledge of different accuracy, and can whiten the observed interference data to obtain the optimal solution. In addition, the extended factored approach (EFA) is used in the optimization for dimensionality reduction, which reduces the computational burden. Simulation results show that the proposed algorithm can effectively improve STAP performance even under the condition of some errors in prior knowledge.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.7 pp.1002-1006
- Publication Date
- 2023/07/01
- Publicized
- 2022/12/27
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022EAL2042
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Quanxin MA
Yantai University
Xiaolin DU
Yantai University
Jianbo LI
Chongqing University of Posts and Telecommunications
Yang JING
Yantai University
Yuqing CHANG
Yantai University
Keyword
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Quanxin MA, Xiaolin DU, Jianbo LI, Yang JING, Yuqing CHANG, "Persymmetric Structured Covariance Matrix Estimation Based on Whitening for Airborne STAP" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 7, pp. 1002-1006, July 2023, doi: 10.1587/transfun.2022EAL2042.
Abstract: The estimation problem of structured clutter covariance matrix (CCM) in space-time adaptive processing (STAP) for airborne radar systems is studied in this letter. By employing the prior knowledge and the persymmetric covariance structure, a new estimation algorithm is proposed based on the whitening ability of the covariance matrix. The proposed algorithm is robust to prior knowledge of different accuracy, and can whiten the observed interference data to obtain the optimal solution. In addition, the extended factored approach (EFA) is used in the optimization for dimensionality reduction, which reduces the computational burden. Simulation results show that the proposed algorithm can effectively improve STAP performance even under the condition of some errors in prior knowledge.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAL2042/_p
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@ARTICLE{e106-a_7_1002,
author={Quanxin MA, Xiaolin DU, Jianbo LI, Yang JING, Yuqing CHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Persymmetric Structured Covariance Matrix Estimation Based on Whitening for Airborne STAP},
year={2023},
volume={E106-A},
number={7},
pages={1002-1006},
abstract={The estimation problem of structured clutter covariance matrix (CCM) in space-time adaptive processing (STAP) for airborne radar systems is studied in this letter. By employing the prior knowledge and the persymmetric covariance structure, a new estimation algorithm is proposed based on the whitening ability of the covariance matrix. The proposed algorithm is robust to prior knowledge of different accuracy, and can whiten the observed interference data to obtain the optimal solution. In addition, the extended factored approach (EFA) is used in the optimization for dimensionality reduction, which reduces the computational burden. Simulation results show that the proposed algorithm can effectively improve STAP performance even under the condition of some errors in prior knowledge.},
keywords={},
doi={10.1587/transfun.2022EAL2042},
ISSN={1745-1337},
month={July},}
Copy
TY - JOUR
TI - Persymmetric Structured Covariance Matrix Estimation Based on Whitening for Airborne STAP
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1002
EP - 1006
AU - Quanxin MA
AU - Xiaolin DU
AU - Jianbo LI
AU - Yang JING
AU - Yuqing CHANG
PY - 2023
DO - 10.1587/transfun.2022EAL2042
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2023
AB - The estimation problem of structured clutter covariance matrix (CCM) in space-time adaptive processing (STAP) for airborne radar systems is studied in this letter. By employing the prior knowledge and the persymmetric covariance structure, a new estimation algorithm is proposed based on the whitening ability of the covariance matrix. The proposed algorithm is robust to prior knowledge of different accuracy, and can whiten the observed interference data to obtain the optimal solution. In addition, the extended factored approach (EFA) is used in the optimization for dimensionality reduction, which reduces the computational burden. Simulation results show that the proposed algorithm can effectively improve STAP performance even under the condition of some errors in prior knowledge.
ER -
English
