Optimal loading factor for minimal sample support space-time adaptive radar

Y. L. Kim, Unnikrishna Pillai, J. R. Guerci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A major issue in space-time adaptive processing (STAP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic interference subspace removal (ISR) technique, which is a computationally and analytically useful form of diagonally loaded SMI method, is derived here. It covers from Hung-Turner Projection (HTP) algorithm to matched filter according to the loading factor. Also the optimum loading factor which gives the maximum signal-to-interference-plus-noise ratio (SINR) is derived here from the viewpoint of singular value decomposition of the covariance matrix. The simulation results with synthetic data show that the maximum SINR indeed coincides with the proposed optimum loading factor in various data sample situations.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherIEEE
Pages2505-2508
Number of pages4
Volume4
StatePublished - 1998
EventProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. Part 1 (of 6) - Seattler, WA, USA
Duration: May 12 1998May 15 1998

Other

OtherProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. Part 1 (of 6)
CitySeattler, WA, USA
Period5/12/985/15/98

Fingerprint

Space time adaptive processing
radar
Radar
Covariance matrix
space-time adaptive processing
interference
Matched filters
Singular value decomposition
Beamforming
moving target indicators
inversions
matched filters
beamforming
estimating
projection
decomposition
simulation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics
  • Software

Cite this

Kim, Y. L., Pillai, U., & Guerci, J. R. (1998). Optimal loading factor for minimal sample support space-time adaptive radar. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 4, pp. 2505-2508). IEEE.

Optimal loading factor for minimal sample support space-time adaptive radar. / Kim, Y. L.; Pillai, Unnikrishna; Guerci, J. R.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4 IEEE, 1998. p. 2505-2508.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, YL, Pillai, U & Guerci, JR 1998, Optimal loading factor for minimal sample support space-time adaptive radar. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 4, IEEE, pp. 2505-2508, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. Part 1 (of 6), Seattler, WA, USA, 5/12/98.
Kim YL, Pillai U, Guerci JR. Optimal loading factor for minimal sample support space-time adaptive radar. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4. IEEE. 1998. p. 2505-2508
Kim, Y. L. ; Pillai, Unnikrishna ; Guerci, J. R. / Optimal loading factor for minimal sample support space-time adaptive radar. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4 IEEE, 1998. pp. 2505-2508
@inproceedings{4df0c4adbf7e4fc9b2a14cb95596e91b,
title = "Optimal loading factor for minimal sample support space-time adaptive radar",
abstract = "A major issue in space-time adaptive processing (STAP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic interference subspace removal (ISR) technique, which is a computationally and analytically useful form of diagonally loaded SMI method, is derived here. It covers from Hung-Turner Projection (HTP) algorithm to matched filter according to the loading factor. Also the optimum loading factor which gives the maximum signal-to-interference-plus-noise ratio (SINR) is derived here from the viewpoint of singular value decomposition of the covariance matrix. The simulation results with synthetic data show that the maximum SINR indeed coincides with the proposed optimum loading factor in various data sample situations.",
author = "Kim, {Y. L.} and Unnikrishna Pillai and Guerci, {J. R.}",
year = "1998",
language = "English (US)",
volume = "4",
pages = "2505--2508",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "IEEE",

}

TY - GEN

T1 - Optimal loading factor for minimal sample support space-time adaptive radar

AU - Kim, Y. L.

AU - Pillai, Unnikrishna

AU - Guerci, J. R.

PY - 1998

Y1 - 1998

N2 - A major issue in space-time adaptive processing (STAP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic interference subspace removal (ISR) technique, which is a computationally and analytically useful form of diagonally loaded SMI method, is derived here. It covers from Hung-Turner Projection (HTP) algorithm to matched filter according to the loading factor. Also the optimum loading factor which gives the maximum signal-to-interference-plus-noise ratio (SINR) is derived here from the viewpoint of singular value decomposition of the covariance matrix. The simulation results with synthetic data show that the maximum SINR indeed coincides with the proposed optimum loading factor in various data sample situations.

AB - A major issue in space-time adaptive processing (STAP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic interference subspace removal (ISR) technique, which is a computationally and analytically useful form of diagonally loaded SMI method, is derived here. It covers from Hung-Turner Projection (HTP) algorithm to matched filter according to the loading factor. Also the optimum loading factor which gives the maximum signal-to-interference-plus-noise ratio (SINR) is derived here from the viewpoint of singular value decomposition of the covariance matrix. The simulation results with synthetic data show that the maximum SINR indeed coincides with the proposed optimum loading factor in various data sample situations.

UR - http://www.scopus.com/inward/record.url?scp=0031636197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031636197&partnerID=8YFLogxK

M3 - Conference contribution

VL - 4

SP - 2505

EP - 2508

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

PB - IEEE

ER -