Cooperative Multitask Learning for Sparsity-Driven SAR Imagery and Nonsystematic Error Autocalibration
| Autor: | Li Pucheng, Lifan Zhao, Lei Yang, Mengdao Xing, Song Zhou, Su Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
Propagation of uncertainty Radar tracker Computer science Radar imaging 0211 other engineering and technologies General Earth and Planetary Sciences Multi-task learning Entropy (information theory) 02 engineering and technology Electrical and Electronic Engineering Algorithm 021101 geological & geomatics engineering |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 58:5132-5147 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2020.2972972 |
| Popis: | Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the sensitivity of nonsystematic errors and highly computational load. In this article, a cooperative multitask learning algorithm is proposed based on an autocalibrated alternating direction method of multipliers (AutoCal-ADMM) framework, by which the sparse feature of the scenes/targets-of-interests can be enhanced, and simultaneously the nonmodeled motion errors of either airborne platform or moving target can be autocalibrated in a synergistic manner. By leveraging the entropy and sparsity regularizers in the AutoCal-ADMM framework, the proposed algorithm is particularly tailored to obtain focused SAR images with enhanced sparsity. A reasonable surrogate function is designed for a convex objective function, so that an analytical proximal mapping of the entropy regularizer can be derived. Both nonsystematic range cell migration (NsRCM) and azimuthal phase errors (APEs) are concerned and coherently compensated. A linear and complex soft-thresholding operator is introduced for the sparse solution. The proposed algorithm is capable of greatly alleviating “error propagation” between multiple tasks, where an optima balance between the sparse and focusing features can be achieved. Superior performance in terms of convergence and efficiency can be guaranteed. Both raw SAR and canonical ground moving target imaging (GMTIm) data sets are processed and comparisons with conventions are performed, where the effectiveness and superiority of the proposed AutoCal-ADMM algorithm are validated. |
| Databáze: | OpenAIRE |
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