Robust two-stage reduced-dimension STAP algorithm and its performance analysis

Yuanzhang Fan; Yongzu Liu; Jianping An; Xiangyuan Bu

doi:10.21629/JSEE.2016.05.02

Robust two-stage reduced-dimension STAP algorithm and its performance analysis

Yuanzhang Fan, Yongzu Liu, Jianping An, Xiangyuan Bu^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

A new two-stage reduced-dimension space-time adaptive processing (STAP) approach, which combines the subcoherent processing interval (sub-CPI) STAP and the principal component analysis (PCA), is proposed to achieve a more enhanced convergence measure of effectiveness (MOE). Furthermore, in the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper (CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.

Original language	English
Article number	7784177
Pages (from-to)	954-960
Number of pages	7
Journal	Journal of Systems Engineering and Electronics
Volume	27
Issue number	5
DOIs	https://doi.org/10.21629/JSEE.2016.05.02
Publication status	Published - Oct 2016

Keywords

convergence measure of effectiveness
covariance matrix taper
reduced-dimensiontechnique
space-time adaptive processing

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10.21629/JSEE.2016.05.02

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title = "Robust two-stage reduced-dimension STAP algorithm and its performance analysis",

abstract = "A new two-stage reduced-dimension space-time adaptive processing (STAP) approach, which combines the subcoherent processing interval (sub-CPI) STAP and the principal component analysis (PCA), is proposed to achieve a more enhanced convergence measure of effectiveness (MOE). Furthermore, in the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper (CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.",

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author = "Yuanzhang Fan and Yongzu Liu and Jianping An and Xiangyuan Bu",

note = "Publisher Copyright: {\textcopyright} 2016 Beijing Institute of Aerospace Information.",

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T1 - Robust two-stage reduced-dimension STAP algorithm and its performance analysis

AU - Fan, Yuanzhang

AU - Liu, Yongzu

AU - An, Jianping

AU - Bu, Xiangyuan

PY - 2016/10

Y1 - 2016/10

N2 - A new two-stage reduced-dimension space-time adaptive processing (STAP) approach, which combines the subcoherent processing interval (sub-CPI) STAP and the principal component analysis (PCA), is proposed to achieve a more enhanced convergence measure of effectiveness (MOE). Furthermore, in the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper (CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.

AB - A new two-stage reduced-dimension space-time adaptive processing (STAP) approach, which combines the subcoherent processing interval (sub-CPI) STAP and the principal component analysis (PCA), is proposed to achieve a more enhanced convergence measure of effectiveness (MOE). Furthermore, in the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper (CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.

KW - convergence measure of effectiveness

KW - covariance matrix taper

KW - reduced-dimensiontechnique

KW - space-time adaptive processing

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JO - Journal of Systems Engineering and Electronics

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Robust two-stage reduced-dimension STAP algorithm and its performance analysis

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