Range-Doppler ISAR Imaging Using SFCW and Chirp Pulse

2021 ◽  
pp. 197-204
Author(s):  
Nagajyothi Aggala ◽  
G. V. Sai Swetha ◽  
Anjali Reddy Pulagam
Keyword(s):  
Chirp Pulse ◽  
Isar Imaging

Range Doppler ISAR Imaging Using Chirp Pulse

2020 ◽  
pp. 35-47
Author(s):  
G. V. Sai Swetha ◽  
P. Anjali Reddy ◽  
A. Naga Jyothi
Keyword(s):  
Chirp Pulse ◽  
Isar Imaging

Weighted Minimum Entropy Autofocus Algorithm for ISAR Imaging

2011 ◽  
Vol 33 (8) ◽  
pp. 1809-1815
Author(s):  
Gang Xu ◽  
Lei Yang ◽  
Lei Zhang ◽  
Ya-chao Li ◽  
Meng-dao Xing
Keyword(s):  
Minimum Entropy ◽  
Isar Imaging ◽  
Autofocus Algorithm

Analysis on the Effect of Nonequivalent Relationship between Space and Time in MIMO-ISAR Imaging

2014 ◽  
Vol 35 (8) ◽  
pp. 1806-1812
Author(s):  
Gang Chen ◽  
Hong Gu ◽  
Wei-min Su ◽  
Hua Shao
Keyword(s):  
Space And Time ◽  
Isar Imaging

Preserving Unique Structural Blocks of Targets in ISAR Imaging by Pitman–Yor Process

2021 ◽  
Vol 21 (2) ◽  
pp. 1859-1876
Author(s):  
Di Cheng ◽  
Bo Yuan ◽  
Yulong Dai ◽  
Chang Chen ◽  
Weidong Chen
Keyword(s):  
Isar Imaging ◽  
Structural Blocks

scholarly journals Development of Real-Time Time Gated Digital (TGD) OFDR Method and Its Performance Verification

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4865
Author(s):  
Kinzo Kishida ◽  
Artur Guzik ◽  
Ken’ichi Nishiguchi ◽  
Che-Hsien Li ◽  
Daiji Azuma ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Fibers ◽  
High Speed ◽  
Oil And Gas ◽  
Scattered Light ◽  
Oil And Gas Industry ◽  
High Signal ◽  
Chirp Pulse ◽  
Sound Waves ◽  
Industry Applications

Distributed acoustic sensing (DAS) in optical fibers detect dynamic strains or sound waves by measuring the phase or amplitude changes of the scattered light. This contrasts with other distributed (and more conventional) methods, such as distributed temperature (DTS) or strain (DSS), which measure quasi-static physical quantities, such as intensity spectrum of the scattered light. DAS is attracting considerable attention as it complements the conventional distributed measurements. To implement DAS in commercial applications, it is necessary to ensure a sufficiently high signal-noise ratio (SNR) for scattered light detection, suppress its deterioration along the sensing fiber, achieve lower noise floor for weak signals and, moreover, perform high-speed processing within milliseconds (or sometimes even less). In this paper, we present a new, real-time DAS, realized by using the time gated digital-optical frequency domain reflectometry (TGD-OFDR) method, in which the chirp pulse is divided into overlapping bands and assembled after digital decoding. The developed prototype NBX-S4000 generates a chirp signal with a pulse duration of 2 μs and uses a frequency sweep of 100 MHz at a repeating frequency of up to 5 kHz. It allows one to detect sound waves at an 80 km fiber distance range with spatial resolution better than a theoretically calculated value of 2.8 m in real time. The developed prototype was tested in the field in various applications, from earthquake detection and submarine cable sensing to oil and gas industry applications. All obtained results confirmed effectiveness of the method and performance, surpassing, in conventional SM fiber, other commercially available interrogators.

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

New Algorithm for Near-Field ISAR Imaging

2020 ◽  
Author(s):  
Jixiang Fu ◽  
Yang Lan ◽  
Mengdao Xing ◽  
Guangcai Sun
Keyword(s):  
Near Field ◽  
Isar Imaging
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