An Anti-Jamming Method against Interrupted Sampling Repeater Jamming Based on Compressed Sensing.

anti-jamming compressed sensing (CS) interrupted sampling repeater jamming (ISRJ) inverse synthetic aperture radar (ISAR)

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
14 Mar 2022
Historique:
received: 15 01 2022
revised: 09 03 2022
accepted: 12 03 2022
entrez: 26 3 2022
pubmed: 27 3 2022
medline: 27 3 2022
Statut: epublish

Résumé

Interrupted sampling repeater jamming (ISRJ) is an attracted coherent jamming method to inverse synthetic aperture radar (ISAR) in the past decades. By means of different jamming parameters settings, realistic dense false targets can be formed around the true target. This paper proposed an adaptive anti-jamming method against ISRJ by adjusting the number of measurements based on compressed sensing (CS). The jamming signal is energy concentrated and segmented sparse in the frequency domain. The measurements number of the reconstructed target signal and the jamming signal is different. According to the restricted isometry property (RIP) condition of CS theory, signal reconstructing performance depends on the number of measurements that varies with the sparsity of the vector. Thus, the jamming signal is suppressed, and the true target signal is retained by altering the measurements number of echo signals. Besides, the two-dimensional (2D) anti-jamming method is derived in detail. The anti-jamming effect is analyzed with different signal-to-noise ratios (SNR), sampling rates, and jam-to-signal ratios (JSR). Simulations prove the effectiveness of the proposed anti-jamming method.

Identifiants

pubmed: 35336416
pii: s22062239
doi: 10.3390/s22062239
pmc: PMC8949386
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Natural Science Foundation of China
ID : 61631019

Références

Sensors (Basel). 2018 Apr 08;18(4):
pubmed: 29642508
Sensors (Basel). 2019 Jul 25;19(15):
pubmed: 31349709

Auteurs

Yingxi Liu (Y)

The Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China.
The Collaborative Innovation Center of Information Sensing and Understanding, Xi'an 710077, China.

Qun Zhang (Q)

The Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China.
The Collaborative Innovation Center of Information Sensing and Understanding, Xi'an 710077, China.
The Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China.

Zhidong Liu (Z)

The Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China.
The Collaborative Innovation Center of Information Sensing and Understanding, Xi'an 710077, China.

Guangming Li (G)

The Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China.

Shichao Xiong (S)

The Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China.

Ying Luo (Y)

The Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China.
The Collaborative Innovation Center of Information Sensing and Understanding, Xi'an 710077, China.

Classifications MeSH