A Novel Phase Error Estimation Method for TomoSAR Imaging Based on Adaptive Momentum Optimizer and Joint Criterion
| Autor: | Muhan Wang, Silin Gao, Xiaolan Qiu, Zhe Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2025 |
| Předmět: | |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 2042-2051 (2025) |
| Druh dokumentu: | article |
| ISSN: | 1939-1404 2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3506852 |
| Popis: | Tomographic synthetic aperture radar (TomoSAR) has garnered significant attention recently due to its 3-D imaging capabilities. However, interchannel phase errors present in practical applications severely degrade the quality of 3-D reconstruction results. The current state-of-the-art methods for interchannel phase error estimation and compensation fall into two categories: calibrator-based methods and data-driven methods relying on synthetic aperture radar echo measurements. These methods face challenges, such as unavailability of the calibrator, reliance on single constraint criteria, and heavy computational burden. In this article, we propose an innovative and efficient method for estimating interchannel phase errors in TomoSAR imaging. We consider the phase errors as parameters to be estimated and optimize the estimation based on a joint constraint criterion of minimum mean square error and minimum entropy. In addition, our approach introduces a joint iterative solution framework, which incorporates a modified accelerated iterative shrinkage-threshold sparse recovery algorithm and an adaptive momentum optimizer for interchannel phase error estimation in a gradient descent manner. Compared to conventional phase error calibration methods in a two-step iterative framework, our proposed method considers image features and parameter coupling relationships, thus achieving higher precision estimation while saving computational costs. Finally, we demonstrate the effectiveness and applicability of the proposed method based on simulation experiments and real data experiments of the SARMV3D dataset. |
| Databáze: | Directory of Open Access Journals |
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