Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar

2018 ◽  
Vol 19 (4) ◽  
pp. 557-568
Author(s):  
Jue Wang ◽  
Jun Wang
Keyword(s):  
Compressed Sensing ◽  
Iterative Method ◽  
Passive Radar ◽  
Phase Adjustment

scholarly journals Compressed Sensing-Based Range-Doppler Processing Method for Passive Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xia Bai ◽  
Hejing Guo ◽  
Juan Zhao ◽  
Tao Shan
Keyword(s):  
Compressed Sensing ◽  
Cross Correlation ◽  
Processing Method ◽  
Sparse Reconstruction ◽  
Digital Television ◽  
Experimental Measurements ◽  
Passive Radar ◽  
Computational Burden ◽  
Chinese Standard ◽  
Classical Cross

Passive radar (PR) systems use the existing transmitters of opportunity in the environment to perform tasks such as detection, tracking, and imaging. The classical cross-correlation based methods to obtain the range-Doppler map have the problems of high sidelobe and limited resolution due to the influence of signal bandwidth. In this paper, we propose a novel range-Doppler processing method based on compressed sensing (CS), which performs sparse reconstruction in range and Doppler dimensions to achieve high resolution and reduces sidelobe without excessive computational burden. Results from numerical simulations and experimental measurements recorded with the Chinese standard digital television terrestrial broadcasting (DTTB) based PR show that the proposed method successfully handles the range-Doppler map formatting problem for PR and outperforms the existing CS-based PR processing methods.

Iterative method with inertial terms for nonexpansive mappings: applications to compressed sensing

2019 ◽  
Vol 83 (4) ◽  
pp. 1321-1347 ◽  
Author(s):  
Yekini Shehu ◽  
Olaniyi S. Iyiola ◽  
Ferdinard U. Ogbuisi
Keyword(s):  
Compressed Sensing ◽  
Iterative Method ◽  
Nonexpansive Mappings ◽  
Inertial Terms

scholarly journals Microwave Bone Imaging: A Preliminary Investigation on Numerical Bone Phantoms for Bone Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6320
Author(s):  
Bilal Amin ◽  
Atif Shahzad ◽  
Martin O’Halloran ◽  
Muhammad Adnan Elahi
Keyword(s):  
Dielectric Properties ◽  
Compressed Sensing ◽  
Iterative Method ◽  
Inverse Scattering ◽  
Bone Health ◽  
Preliminary Investigation ◽  
Scattering Problem ◽  
Inverse Scattering Problem ◽  
Linear Inversion ◽  
Trabecular Bones

Microwave tomography (MWT) can be used as an alternative modality for monitoring human bone health. Studies have found a significant dielectric contrast between healthy and diseased human trabecular bones. A set of diverse bone phantoms were developed based on single-pole Debye parameters of osteoporotic and osteoarthritis human trabecular bones. The bone phantoms were designed as a two-layered circular structure, where the outer layer mimics the dielectric properties of the cortical bone and the inner layer mimics the dielectric properties of the trabecular bone. The electromagnetic (EM) inverse scattering problem was solved using a distorted Born iterative method (DBIM). A compressed sensing-based linear inversion approach referred to as iterative method with adaptive thresholding for compressed sensing (IMATCS) has been employed for solving the underdetermined set of linear equations at each DBIM iteration. To overcome the challenges posed by the ill-posedness of the EM inverse scattering problem, the L2-based regularization approach was adopted in the amalgamation of the IMATCS approach. The simulation results showed that osteoporotic and osteoarthritis bones can be differentiated based on the reconstructed dielectric properties even for low values of the signal-to-noise ratio. These results show that the adopted approach can be used to monitor bone health based on the reconstructed dielectric properties.

Phase adjustment for multistatic passive radar imaging based on image entropy and image contrast

2016 ◽  
Vol 37 (18) ◽  
pp. 4460-4485
Author(s):  
Jue Wang ◽  
Jun Wang ◽  
Yong Wu ◽  
Yun Zhu ◽  
Zhen Luo ◽  
...  
Keyword(s):  
Radar Imaging ◽  
Image Contrast ◽  
Passive Radar ◽  
Phase Adjustment ◽  
Image Entropy
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