Clutter covariance matrix estimation using weight vectors in knowledge-aided STAP

H. Jeon, Y. Chung, Wonzoo Chung, J. Kim, H. Yang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A knowledge-aided space-time adaptive processing (STAP) is a quite useful technique to suppress non-stationary and heterogeneous clutter. It estimates a covariance matrix by combining a conventional covariance matrix based on secondary data with a synthesised one by prior information. A new combining method is presented, where weight vectors, rather than constant weights, are used to combine two covariance matrices. In this method, the weight vectors are derived in a way to maximise clutter-to-noise ratio of the combined covariance matrix. A numerical simulation is conducted for a bistatic radar scenario where clutter non-stationarity and heterogeneity can be assumed and the performance of the proposed method is demonstrated in terms of clutter suppression and target detection.

Original languageEnglish
Pages (from-to)560-562
Number of pages3
JournalElectronics Letters
Volume53
Issue number8
DOIs
Publication statusPublished - 2017 Apr 13

Fingerprint

Space time adaptive processing
Covariance matrix
Target tracking
Radar
Computer simulation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Clutter covariance matrix estimation using weight vectors in knowledge-aided STAP. / Jeon, H.; Chung, Y.; Chung, Wonzoo; Kim, J.; Yang, H.

In: Electronics Letters, Vol. 53, No. 8, 13.04.2017, p. 560-562.

Research output: Contribution to journalArticle

Jeon, H. ; Chung, Y. ; Chung, Wonzoo ; Kim, J. ; Yang, H. / Clutter covariance matrix estimation using weight vectors in knowledge-aided STAP. In: Electronics Letters. 2017 ; Vol. 53, No. 8. pp. 560-562.
@article{eddeaf3f9d704030bc9a6420a5c814ea,
title = "Clutter covariance matrix estimation using weight vectors in knowledge-aided STAP",
abstract = "A knowledge-aided space-time adaptive processing (STAP) is a quite useful technique to suppress non-stationary and heterogeneous clutter. It estimates a covariance matrix by combining a conventional covariance matrix based on secondary data with a synthesised one by prior information. A new combining method is presented, where weight vectors, rather than constant weights, are used to combine two covariance matrices. In this method, the weight vectors are derived in a way to maximise clutter-to-noise ratio of the combined covariance matrix. A numerical simulation is conducted for a bistatic radar scenario where clutter non-stationarity and heterogeneity can be assumed and the performance of the proposed method is demonstrated in terms of clutter suppression and target detection.",
author = "H. Jeon and Y. Chung and Wonzoo Chung and J. Kim and H. Yang",
year = "2017",
month = "4",
day = "13",
doi = "10.1049/el.2016.4631",
language = "English",
volume = "53",
pages = "560--562",
journal = "Electronics Letters",
issn = "0013-5194",
publisher = "Institution of Engineering and Technology",
number = "8",

}

TY - JOUR

T1 - Clutter covariance matrix estimation using weight vectors in knowledge-aided STAP

AU - Jeon, H.

AU - Chung, Y.

AU - Chung, Wonzoo

AU - Kim, J.

AU - Yang, H.

PY - 2017/4/13

Y1 - 2017/4/13

N2 - A knowledge-aided space-time adaptive processing (STAP) is a quite useful technique to suppress non-stationary and heterogeneous clutter. It estimates a covariance matrix by combining a conventional covariance matrix based on secondary data with a synthesised one by prior information. A new combining method is presented, where weight vectors, rather than constant weights, are used to combine two covariance matrices. In this method, the weight vectors are derived in a way to maximise clutter-to-noise ratio of the combined covariance matrix. A numerical simulation is conducted for a bistatic radar scenario where clutter non-stationarity and heterogeneity can be assumed and the performance of the proposed method is demonstrated in terms of clutter suppression and target detection.

AB - A knowledge-aided space-time adaptive processing (STAP) is a quite useful technique to suppress non-stationary and heterogeneous clutter. It estimates a covariance matrix by combining a conventional covariance matrix based on secondary data with a synthesised one by prior information. A new combining method is presented, where weight vectors, rather than constant weights, are used to combine two covariance matrices. In this method, the weight vectors are derived in a way to maximise clutter-to-noise ratio of the combined covariance matrix. A numerical simulation is conducted for a bistatic radar scenario where clutter non-stationarity and heterogeneity can be assumed and the performance of the proposed method is demonstrated in terms of clutter suppression and target detection.

UR - http://www.scopus.com/inward/record.url?scp=85017560534&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017560534&partnerID=8YFLogxK

U2 - 10.1049/el.2016.4631

DO - 10.1049/el.2016.4631

M3 - Article

AN - SCOPUS:85017560534

VL - 53

SP - 560

EP - 562

JO - Electronics Letters

JF - Electronics Letters

SN - 0013-5194

IS - 8

ER -