Sparsely corrupted stimulated scattering signals recovery by iterative reweighted continuous basis pursuit
- College of Automation, Chongqing University, Chongqing 400044 (China)
- Research Center of Laser Fusion, CAEP, P. O. Box 919-983, Mianyang 621900 (China)
In this paper, we consider the problem of extracting the desired signals from noisy measurements. This is a classical problem of signal recovery which is of paramount importance in inertial confinement fusion. To accomplish this task, we develop a tractable algorithm based on continuous basis pursuit and reweighted ℓ{sub 1}-minimization. By modeling the observed signals as superposition of scale time-shifted copies of theoretical waveform, structured noise, and unstructured noise on a finite time interval, a sparse optimization problem is obtained. We propose to solve this problem through an iterative procedure that alternates between convex optimization to estimate the amplitude, and local optimization to estimate the dictionary. The performance of the method was evaluated both numerically and experimentally. Numerically, we recovered theoretical signals embedded in increasing amounts of unstructured noise and compared the results with those obtained through popular denoising methods. We also applied the proposed method to a set of actual experimental data acquired from the Shenguang-II laser whose energy was below the detector noise-equivalent energy. Both simulation and experiments show that the proposed method improves the signal recovery performance and extends the dynamic detection range of detectors.
- OSTI ID:
- 22220465
- Journal Information:
- Review of Scientific Instruments, Vol. 84, Issue 8; Other Information: (c) 2013 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0034-6748
- Country of Publication:
- United States
- Language:
- English
Similar Records
Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations
SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM