Results 41 to 50 of about 6,651 (246)

A sparsity-driven approach for joint SAR imaging and phase error correction [PDF]

open access: yes, 2012
Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed ...
Çetin, Müjdat, Önhon, Özben Naime
core   +1 more source

Transient Scattering Echo Simulation and ISAR Imaging for a Composite Target-Ocean Scene Based on the TDSBR Method

open access: yesRemote Sensing, 2022
We propose an inverse synthetic aperture radar (ISAR) imaging algorithm for a composite target-ocean scene based on time-domain shooting and bouncing rays (TDSBR) method.
Guangbin Guo   +4 more
doaj   +1 more source

Subinteger Range-Bin Alignment Method for ISAR Imaging of Noncooperative Targets

open access: yesEURASIP Journal on Advances in Signal Processing, 2010
Inverse Synthetic Aperture Radar (ISAR) is a coherent radar technique capable of generating images of noncooperative targets. ISAR may have better performance in adverse meteorological conditions than traditional imaging sensors.
Pérez-Martínez F   +1 more
doaj   +2 more sources

ISAR Image formation with a combined Empirical Mode Decomposition and Time-Frequency Representation

open access: yes, 2015
International audienceIn this paper, a method for Inverse Synthetic Aperture Radar (ISAR) image formation based on the use of the Complex Empirical Mode Decomposition (CEMD) is proposed.
Bay Ahmed , Ahmed Hadj   +2 more
core   +2 more sources

Spherical wave near-field imaging and radar cross-section measurement [PDF]

open access: yes, 1998
The paper presents a new inverse synthetic aperture radar (ISAR) algorithm intended for radar cross-section (RCS) imaging and measurement from scattered fields.
Broquetas Ibars, Antoni   +3 more
core   +2 more sources

Compressive sensing approach for high-resolution ISAR image reconstruction and autofocus

open access: yesThe Journal of Engineering, 2019
In this study, a novel autofocus method is presented to achieve high-resolution inverse synthetic aperture radar (ISAR) image reconstruction with limited measurements in the compressive sensing (CS) framework.
Min-Seok Kang, Kyung-Tae Kim
doaj   +1 more source

Imaging of High-Speed Aerial Targets With ISAR Installed on a Moving Vessel

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
An inverse synthetic aperture radar (ISAR) technique is proposed to acquire high-resolution steady images of high-speed aerial target with radar installed on a moving vessel.
Dau-Ming Wu, Jean-Fu Kiang
doaj   +1 more source

Experimental Results of High-Resolution ISAR Imaging of Ground-Moving Vehicles with a Stationary FMCW Rada [PDF]

open access: yes, 2015
In the paper experimental results of ISAR (Inverse Synthetic Aperture Radar) processing obtained with highresolution radar are presented. Targets under observation were ground moving vehicles, such as cars, trucks and tractors.
Gromek, Artur   +5 more
core   +1 more source

An ISAR target motion estimation algorithm based on a differential semblance criterion

open access: yesElectronics Letters, Volume 61, Issue 1, January/December 2025.
This study presents a novel approach for improving rotational motion estimation in multistatic Inverse Synthetic Aperture Radar (ISAR) imaging through a Differential Semblance Optimization (DSO) criterion. By reducing discrepancies across images from multiple transmitter–receiver pairs, our method enables precise yaw rotation estimation and produces ...
D. P. Huxley   +2 more
wiley   +1 more source

Joint Noise Suppression and Resolution Enhancement of ISAR Images Using Integrated Neural Networks

open access: yesElectronics Letters, Volume 61, Issue 1, January/December 2025.
This study proposes an integrated neural network for simultaneous noise suppression and resolution enhancement in inverse synthetic aperture radar (ISAR) images. The proposed approach sequentially combines separate generative models for each task and optimizes them using a joint learning strategy.
Seonmin Cho   +5 more
wiley   +1 more source

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