scholarly journals Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

2021 ◽  
pp. 1-1
Author(s):  
Sean Thammakhoune ◽  
Bariscan Yonel ◽  
Eric Mason ◽  
Birsen Yazici ◽  
Yonina C. Eldar
Keyword(s):  
Phase Space ◽  
Convex Optimization ◽  
Moving Target ◽  
Function Recovery ◽  
Target Imaging ◽  
Space Function ◽  
Moving Target Imaging ◽  
Phase Space Function

scholarly journals Ground Moving Target Imaging via SDAP-ISAR Processing: Review and New Trends

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2391
Author(s):  
Marco Martorella ◽  
Samuele Gelli ◽  
Alessio Bacci
Keyword(s):  
Homeland Security ◽  
Moving Target ◽  
Security Applications ◽  
Ground Clutter ◽  
Radar Systems ◽  
Joint Application ◽  
Target Imaging ◽  
Moving Target Imaging ◽  
Space Time Adaptive Processing ◽  
Border Surveillance

Ground moving target imaging finds its main applications in both military and homeland security applications, with examples in operations of intelligence, surveillance and reconnaissance (ISR) as well as border surveillance. When such an operation is performed from the air looking down towards the ground, the clutter return may be comparable or even stronger than the target’s, making the latter hard to be detected and imaged. In order to solve this problem, multichannel radar systems are used that are able to remove the ground clutter and effectively detect and image moving targets. In this feature paper, the latest findings in the area of Ground Moving Target Imaging are revisited that see the joint application of Space-Time Adaptive Processing and Inverse Synthetic Aperture Radar Imaging. The theoretical aspects analysed in this paper are supported by practical evidence and followed by application-oriented discussions.

scholarly journals Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Ahmed Shaharyar Khwaja ◽  
Muhammad Naeem ◽  
Alagan Anpalagan
Keyword(s):  
Iterative Scheme ◽  
Reconstruction Error ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Target Imaging ◽  
Cause Of Error ◽  
Moving Target Imaging ◽  
Velocity Mismatch ◽  
Object Imaging ◽  
Better Than

We present compressed sensing (CS) synthetic aperture radar (SAR) moving target imaging in the presence of dictionary mismatch. Unlike existing work on CS SAR moving target imaging, we analyze the sensitivity of the imaging process to the mismatch and present an iterative scheme to cope with dictionary mismatch. We analyze and investigate the effects of mismatch in range and azimuth positions, as well as range velocity. The analysis reveals that the reconstruction error increases with the mismatch and range velocity mismatch is the major cause of error. Instead of using traditional Laplacian prior (LP), we use Gaussian-Bernoulli prior (GBP) for CS SAR imaging mismatch. The results show that the performance of GBP is much better than LP. We also provide the Cramer-Rao Bounds (CRB) that demonstrate theoretically the lowering of mean square error between actual and reconstructed result by using the GBP. We show that a combination of an upsampled dictionary and the GBP for reconstruction can deal with position mismatch effectively. We further present an iterative scheme to deal with the range velocity mismatch. Numerical and simulation examples demonstrate the accuracy of the analysis as well as the effectiveness of the proposed upsampling and iterative scheme.

SAR Slow Moving Target Imaging Based on Over-Sampling Smooth Algorithm

2017 ◽  
Vol 26 (4) ◽  
pp. 876-882 ◽  
Author(s):  
Hongyin Shi ◽  
Xiaoyan Yang ◽  
Qiuxiao Zhou ◽  
Qiusheng Lian
Keyword(s):  
Moving Target ◽  
Target Imaging ◽  
Moving Target Imaging ◽  
Slow Moving

Adaptive Sparse Recovery of Moving Targets for Distributed MIMO Radar

2014 ◽  
Vol 933 ◽  
pp. 450-455
Author(s):  
Hui Yu ◽  
Guang Hua Lu ◽  
Hai Long Zhang
Keyword(s):  
High Resolution ◽  
Sparse Recovery ◽  
Mimo Radar ◽  
Moving Target ◽  
Recovery Algorithm ◽  
Low Snr ◽  
Computation Efficiency ◽  
Target Imaging ◽  
Moving Target Imaging ◽  
Recovery Error

The high resolution and better recovery performance with distributed MIMO radar would be significantly degraded when the target moves at an unknown velocity. In this paper, we propose an adaptive sparse recovery algorithm for moving target imaging to estimate the velocity and image jointly with high computation efficiency. With an iteration mechanism, the proposed method updates the image and estimates the velocity alternately by sequentially minimizing the norm and the recovery error. Numerical simulations are carried out to demonstrate that the proposed algorithm can retrieve high-resolution image and accurate velocity simultaneously even in low SNR.

Sign in / Sign up

Export Citation Format

Share Document