Next Article in Journal
A Novel Scheme and Evaluations on a Long-Term and Continuous Biosensor Platform Integrated with a Dental Implant Fixture and Its Prosthetic Abutment
Previous Article in Journal
Onboard Image Processing System for Hyperspectral Sensor
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(10), 24945-24960; doi:10.3390/s151024945

A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field

1
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
Institute of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang 212000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 12 May 2015 / Revised: 7 September 2015 / Accepted: 23 September 2015 / Published: 25 September 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [1068 KB, uploaded 25 September 2015]   |  

Abstract

The Compressive Sensing (CS) approach has proven to be useful for Synthetic Aperture Interferometric Radiometer (SAIR) imaging because it provides the same high-resolution capability while using part of interferometric observations compared to traditional methods using the entirety. However, it cannot always obtain the sparsest solution and may yield outliers with the non-adaptive random measurement matrix adopted by current CS models. To solve those problems, this paper proposes a robust reweighted L1-minimization imaging algorithm, called RRIA, to reconstruct images accurately by combining the sparsity and prior information of SAIR images in near field. RRIA employs iterative reweighted L1-minimization to enhance the sparsity to reconstruct SAIR images by computing a new weight factor in each iteration according to the previous SAIR images. Prior information estimated by the energy functional of SAIR images is introduced to RRIA as an additional constraint condition to make the algorithm more robust for different complex scenes. Compared to the current basic CS approach, our simulation results indicate that RRIA can achieve better recovery with the same amount of interferometric observations. Experimental results of different scenes demonstrate the validity and robustness of RRIA. View Full-Text
Keywords: passive millimeter wave; SAIR; reweighted L1-minimization; prior information passive millimeter wave; SAIR; reweighted L1-minimization; prior information
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, Y.; Li, Y.; Zhu, S.; Li, Y. A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field. Sensors 2015, 15, 24945-24960.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top