Image and Signal Processing Methods

Expansion–compression variance-component-based autofocusing method for joint radar coincidence imaging and gain–phase error calibration

[+] Author Affiliations
Xiaoli Zhou, Hongqiang Wang, Yongqiang Cheng, Yuliang Qin

National University of Defense Technology, Research Institute of Space Electronics, College of Electronic Science and Engineering, Changsha, China

J. Appl. Remote Sens. 11(2), 025002 (Apr 19, 2017). doi:10.1117/1.JRS.11.025002
History: Received November 23, 2016; Accepted April 6, 2017
Text Size: A A A

Abstract.  Radar coincidence imaging (RCI) is a super-resolution staring technique based on the innovative idea of random radiation and wavefront random modulation. To reconstruct the target, sparsity-driven methods are commonly used in RCI, while the prior knowledge of the imaging model requires to be known accurately. However, model error generally exists, which induces the inaccuracy of the model and defocuses the image. We focus on sparsity-driven RCI in the presence of gain–phase error and propose an autocalibration expansion–compression variance-component (AC-ExCoV)-based autofocusing method in a sparse Bayesian learning framework. The algorithm determines the gain–phase error as a part of the RCI process by reconstructing the target and compensating the gain–phase error iteratively. To probabilistically formulate the target reconstruction problem, a probabilistic model is utilized to fully exploit the sparse prior, and then solved using ExCoV. Meanwhile, the gain–phase error is estimated and calibrated to obtain a high-resolution focused image. The AC-ExCoV algorithm demands no prior knowledge about the sparsity or measurement-noise level with significant superiority in computational complexity. Simulation results show that the proposed algorithm obtains a well-focused target image with high reconstruction accuracy.

Figures in this Article
© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Xiaoli Zhou ; Hongqiang Wang ; Yongqiang Cheng and Yuliang Qin
"Expansion–compression variance-component-based autofocusing method for joint radar coincidence imaging and gain–phase error calibration", J. Appl. Remote Sens. 11(2), 025002 (Apr 19, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.025002


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.