A Multi-Parameter Calibration Method Based on the Newton Method and the Genetic Algorithm in Airborne Array Synthetic Aperture Radar

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 24; p. 4677
• Main Authors: Wang, Dawei, Li, Zhenhua, Zhang, Fubo, Chen, Longyong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: 3D imaging; airborne array SAR; Airborne radar; Aircraft; Algorithms; Antennas; Artificial satellites in remote sensing; Calibration; Convergence; Errors; genetic algorithm; Genetic algorithms; Genetic research; Global optimization; Global positioning systems; GPS; Image processing; Image resolution; Maximum likelihood method; Methods; multi-parameter calibration; Newton method; Newton methods; Parameter robustness; Radar; Radar arrays; Synthetic aperture radar; Three dimensional imaging; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16244677

Airborne array synthetic aperture radar (SAR) can achieve three-dimensional (3D) imaging of the observed scene in a single flight. Nevertheless, the imaging process of airborne array SAR is subject to various parameter errors due to unstable factors. Such errors degrade the quality of 3D imaging, particularly for the elevation imaging results, which necessitates the employment of super-resolution algorithms. The most significant error parameters include the amplitude and phase imbalances between multiple channels, as well as the phase-center positions of each channel. Owing to the coupled nature of these parameter errors, the calibration accuracy for each parameter independently is relatively sub-par, while super-resolution algorithms have strict demands for parameter precision. Addressing these challenges, this article proposes a multi-parameter calibration method for airborne array SAR based on the Newton method and the genetic algorithm. Initially, a least squares model for multi-parameter calibration is established, followed by leveraging the global optimization characteristics of genetic algorithms and the rapid convergence property of the Newton method. The genetic algorithm is utilized to locate a sub-optimal solution in proximity to the optimal one, subsequently converging swiftly to the optimal solution via the Newton method, which incorporates second-order information. This approach averts the pitfalls of local convergence due to large initial value errors, thereby enhancing the algorithm’s robustness. The proposed method effectively enhances the precision of multi-parameter calibration, which is of significant importance in ensuring the quality of 3D imaging of airborne array SAR.