A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge

Niezipeng Kang; Yun Zhang; Gaopeng Li; Hang Ren

doi:10.1109/IGARSS52108.2023.10282378

A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge

Niezipeng Kang
, Yun Zhang
, Gaopeng Li^*
, Hang Ren

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Space-time adaptive processing (STAP) is widely used for clutter suppression[1]. The key of space-time adaptive processing is the accuracy of clutter covariance matrix estimation. In the airborne radar environment, besides the target of interest, there are many clutter and interference targets in different directions. The existence of these echoes makes the training samples used to estimate the clutter covariance matrix uneven. In this case, the paper proposes a sparse recovery STAP algorithm based on prior knowledge for sample selection to solve existing problems.

Original language	English
Title of host publication	IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4407-4410
Number of pages	4
ISBN (Electronic)	9798350320107
DOIs	https://doi.org/10.1109/IGARSS52108.2023.10282378
State	Published - 2023
Externally published	Yes
Event	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2023-July

Conference

Conference	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/Territory	United States
City	Pasadena
Period	16/07/23 → 21/07/23

Keywords

Space-time adaptive processing (STAP) sparse recovery
airborne multi-channel
clutter suppression

Access to Document

10.1109/IGARSS52108.2023.10282378

Cite this

Kang, N., Zhang, Y., Li, G., & Ren, H. (2023). A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 4407-4410). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS52108.2023.10282378

Kang, Niezipeng ; Zhang, Yun ; Li, Gaopeng et al. / A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 4407-4410 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{404b0a36609c41e4ad0a3672d533ce19,

title = "A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge",

abstract = "Space-time adaptive processing (STAP) is widely used for clutter suppression[1]. The key of space-time adaptive processing is the accuracy of clutter covariance matrix estimation. In the airborne radar environment, besides the target of interest, there are many clutter and interference targets in different directions. The existence of these echoes makes the training samples used to estimate the clutter covariance matrix uneven. In this case, the paper proposes a sparse recovery STAP algorithm based on prior knowledge for sample selection to solve existing problems.",

keywords = "Space-time adaptive processing (STAP) sparse recovery, airborne multi-channel, clutter suppression",

author = "Niezipeng Kang and Yun Zhang and Gaopeng Li and Hang Ren",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",

year = "2023",

doi = "10.1109/IGARSS52108.2023.10282378",

language = "英语",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4407--4410",

booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",

address = "美国",

}

Kang, N, Zhang, Y , Li, G & Ren, H 2023, A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge. in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2023-July, Institute of Electrical and Electronics Engineers Inc., pp. 4407-4410, 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, United States, 16/07/23. https://doi.org/10.1109/IGARSS52108.2023.10282378

A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge. / Kang, Niezipeng; Zhang, Yun ; Li, Gaopeng et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 4407-4410 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge

AU - Kang, Niezipeng

AU - Zhang, Yun

AU - Li, Gaopeng

AU - Ren, Hang

PY - 2023

Y1 - 2023

N2 - Space-time adaptive processing (STAP) is widely used for clutter suppression[1]. The key of space-time adaptive processing is the accuracy of clutter covariance matrix estimation. In the airborne radar environment, besides the target of interest, there are many clutter and interference targets in different directions. The existence of these echoes makes the training samples used to estimate the clutter covariance matrix uneven. In this case, the paper proposes a sparse recovery STAP algorithm based on prior knowledge for sample selection to solve existing problems.

AB - Space-time adaptive processing (STAP) is widely used for clutter suppression[1]. The key of space-time adaptive processing is the accuracy of clutter covariance matrix estimation. In the airborne radar environment, besides the target of interest, there are many clutter and interference targets in different directions. The existence of these echoes makes the training samples used to estimate the clutter covariance matrix uneven. In this case, the paper proposes a sparse recovery STAP algorithm based on prior knowledge for sample selection to solve existing problems.

KW - Space-time adaptive processing (STAP) sparse recovery

KW - airborne multi-channel

KW - clutter suppression

UR - https://www.scopus.com/pages/publications/85178338973

U2 - 10.1109/IGARSS52108.2023.10282378

DO - 10.1109/IGARSS52108.2023.10282378

M3 - 会议稿件

AN - SCOPUS:85178338973

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 4407

EP - 4410

BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023

Y2 - 16 July 2023 through 21 July 2023

ER -

Kang N, Zhang Y , Li G, Ren H. A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 4407-4410. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS52108.2023.10282378

A Novel Multi-Channel Sparse Recovery STAP Algorithm for Sample Selection Based on Prior Knowledge

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Keywords

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