A training sample selection method based on united generalised inner product statistics for STAP

Abstract In heterogeneous environments, the snapshot under test (SUT) and the corresponding training samples are usually not independent and identically distributed, which seriously degrades the clutter suppression performance of space‐time adaptive processing (STAP). To solve this problem, this pap...

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Main Authors: Xinzhe Li, Wenchong Xie, Yongliang Wang
Format: Article
Language:English
Published: Wiley 2021-12-01
Series:IET Radar, Sonar & Navigation
Subjects:
Online Access:https://doi.org/10.1049/rsn2.12146
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author Xinzhe Li
Wenchong Xie
Yongliang Wang
author_facet Xinzhe Li
Wenchong Xie
Yongliang Wang
author_sort Xinzhe Li
collection DOAJ
description Abstract In heterogeneous environments, the snapshot under test (SUT) and the corresponding training samples are usually not independent and identically distributed, which seriously degrades the clutter suppression performance of space‐time adaptive processing (STAP). To solve this problem, this paper proposes a method which can select the training samples with similar clutter characteristics to that of the SUT. The proposed method constructs a novel united generalised inner product (UGIP) statistic with the sub‐aperture clutter covariance matrix (CCM) of the SUT and that of any other snapshot. The smaller the statistic is, the more similar the corresponding two snapshots are. Therefore, the snapshots with smaller UGIPs will be selected as training samples. The proposed method effectively improves the quality of the selected training samples for STAP and a better estimate of the CCM can be obtained. Simulation experiments verify the effectiveness of the proposed method with both simulated data and measured data.
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spelling doaj.art-e615d71be8a640f4a1ba72a8c2497f3b2022-12-22T03:46:49ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922021-12-0115121565157210.1049/rsn2.12146A training sample selection method based on united generalised inner product statistics for STAPXinzhe Li0Wenchong Xie1Yongliang Wang2Key research Lab Wuhan Early Warning Academy Wuhan ChinaKey research Lab Wuhan Early Warning Academy Wuhan ChinaKey research Lab Wuhan Early Warning Academy Wuhan ChinaAbstract In heterogeneous environments, the snapshot under test (SUT) and the corresponding training samples are usually not independent and identically distributed, which seriously degrades the clutter suppression performance of space‐time adaptive processing (STAP). To solve this problem, this paper proposes a method which can select the training samples with similar clutter characteristics to that of the SUT. The proposed method constructs a novel united generalised inner product (UGIP) statistic with the sub‐aperture clutter covariance matrix (CCM) of the SUT and that of any other snapshot. The smaller the statistic is, the more similar the corresponding two snapshots are. Therefore, the snapshots with smaller UGIPs will be selected as training samples. The proposed method effectively improves the quality of the selected training samples for STAP and a better estimate of the CCM can be obtained. Simulation experiments verify the effectiveness of the proposed method with both simulated data and measured data.https://doi.org/10.1049/rsn2.12146airborne radarobject detectioncovariance matricesradar signal processingradar clutterspace‐time adaptive processing
spellingShingle Xinzhe Li
Wenchong Xie
Yongliang Wang
A training sample selection method based on united generalised inner product statistics for STAP
IET Radar, Sonar & Navigation
airborne radar
object detection
covariance matrices
radar signal processing
radar clutter
space‐time adaptive processing
title A training sample selection method based on united generalised inner product statistics for STAP
title_full A training sample selection method based on united generalised inner product statistics for STAP
title_fullStr A training sample selection method based on united generalised inner product statistics for STAP
title_full_unstemmed A training sample selection method based on united generalised inner product statistics for STAP
title_short A training sample selection method based on united generalised inner product statistics for STAP
title_sort training sample selection method based on united generalised inner product statistics for stap
topic airborne radar
object detection
covariance matrices
radar signal processing
radar clutter
space‐time adaptive processing
url https://doi.org/10.1049/rsn2.12146
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