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22273

Compressive Sensing Radar Signal Processing

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Last updated: 24 Dec 2024

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Abstract

This paper presents the application of Compressive Sensing (CS) theory in radar signal processing. CS uses the sparsity property to reduce the number of measurements needed for digital acquisition, which causes reduction in the size, weight, power consumption, and the cost of the CS radar receiver. Complex Approximate Message Passing (CAMP) algorithm is a fast iterative thresholding algorithm which is used to reconstruct the undersampled sparse signal and improves its Signal-to-Noise Ratio (SNR) [12- 16]. In present work, the superiority in performance of applying the CAMP algorithm in radar signal processing compared to the Digital Matched Filter (DMF), and the simple envelope detector is proved through the Receiver Operating characteristic (ROC) curves.

DOI

10.21608/asat.2013.22273

Keywords

Compressive Sensing, CAMP, Radar signal processing

Authors

First Name

F.

Last Name

Ahmed

MiddleName

M.

Affiliation

Egyptian Armed Forces, Egypt.

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First Name

H.

Last Name

Kamel

MiddleName

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Affiliation

Egyptian Armed Forces, Egypt.

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Orcid

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First Name

K.

Last Name

Moustafa

MiddleName

H.

Affiliation

Egyptian Armed Forces, Egypt.

Email

-

City

-

Orcid

-

First Name

M.

Last Name

Hossiny

MiddleName

-

Affiliation

Egyptian Armed Forces, Egypt.

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Volume

15

Article Issue

AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT - 15 – May 28 - 30, 2013

Related Issue

4183

Issue Date

2013-05-01

Receive Date

2018-12-18

Publish Date

2013-05-01

Page Start

1

Page End

10

Print ISSN

2090-0678

Online ISSN

2636-364X

Link

https://asat.journals.ekb.eg/article_22273.html

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https://asat.journals.ekb.eg/service?article_code=22273

Order

119

Type

Original Article

Type Code

737

Publication Type

Journal

Publication Title

International Conference on Aerospace Sciences and Aviation Technology

Publication Link

https://asat.journals.ekb.eg/

MainTitle

Compressive Sensing Radar Signal Processing

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Article

Created At

22 Jan 2023