Synthetic aperture radar autofocus and calibration
a day ago
- #RadarTechnology
- #SAR
- #DroneImaging
- Introduction of a drone-mounted polarimetric synthetic aperture radar (SAR) with improvements in image quality through software enhancements.
- Description of a new SAR autofocus algorithm combining existing methods for drone-mounted SAR applications.
- Explanation of radar signal processing and SAR image formation geometry, including range resolution and phase dependency.
- Detailed SAR image formation process using matched filtering and the impact of position errors on image quality.
- Introduction of the Generalized Phase Gradient Autofocus (GPGA) algorithm for estimating and correcting radar position errors.
- Method for 3D trajectory estimation to correct position errors affecting SAR image focus across different target angles.
- Use of Weighted Least Squares for improving phase error estimation by weighting targets based on signal-to-clutter ratio.
- Implementation of the GPGA algorithm on GPU for efficient processing, enabling quick autofocus even for large datasets.
- Synthetic and real data examples demonstrating the effectiveness of the autofocus algorithm in improving SAR image quality.
- Antenna pattern normalization techniques to correct for variations in received power due to distance and antenna gain.
- Polarimetric calibration methods to correct for channel imbalances and crosstalk in fully polarized radar measurements.
- Application of polarimetric decompositions, such as Pauli decomposition, for interpreting target responses in SAR images.
- Creation of VideoSAR by processing continuous radar measurements into a sequence of autofocused SAR images.
- Urban scene example showcasing the system's capability to produce high-quality SAR images over complex terrains.
- Conclusion highlighting the successful development of a software pipeline for autofocusing and calibrating polarimetric SAR images, with open-source availability.