PR-based SAR reconstruction algorithm for phase noise mitigation based on the hidden convexity |
| |
| Affiliation: | 1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China;2. The Second Research Institute of CAAC, Chengdu, China;1. National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044, China;2. State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China;1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China;2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China;1. Institute of Space Electronics and Information Technology, School of Electronic Science and Engineering, National University of Defense Technology, China;2. Department of Signal Processing and Acoustics, Aalto University, Finland;3. China Academy of Electronics and Information Technology, China Electronics Technology Group Corporation, China;1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China;2. Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi’an 710071, Shaanxi Province, China |
| |
| Abstract: | The performance of synthetic aperture radar (SAR) reconstruction is significantly deteriorated by the random phase noises arising from the atmospheric turbulence or frequency jitter of the transmit signal. Recently, the emerging phase retrieval (PR) technique is gradually extended to the SAR reconstruction problem via the phase-corrupted data attributing to its alluring potential for phase noise mitigation. In this paper, a novel PR-based SAR reconstruction algorithm for phase noise mitigation is proposed by jointing alternating direction method of multipliers (ADMM) and Kolmogorov spectral factorization (KoSF). Owing to the exploiting of the hidden convexity of PR-based SAR reconstruction problem and the structure advantage of the quadratic magnitude measurement, the proposed algorithm acquires better robustness for the complex-valued Gaussian white noises and the random phase noises than the existing PR-based SAR reconstruction algorithms. In the experiments, the synthetic scene data and the moving and stationary target recognition Sandia laboratories implementation of cylinders (MSTAR SLICY) target data are provided to verify the validity of the proposed algorithm. |
| |
| Keywords: | Synthetic aperture radar (SAR) Random phase noises Hidden convexity Alternating direction method of multipliers (ADMM) Kolmogorov spectral factorization (KoSF) |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
