Duration: 36 months
Modern RF EO systems are commonly based on planar array-type antennas and rely more and more on analogue and digital beamforming techniques. During orbit, the antennas experience surface deformation due to continuous exposure to thermal variations, reducing the antenna efficiency [1]. With optimal antenna performance, Synthetic Aperture Radar (SAR) systems maintain SNR, radiometric accuracy and stability, providing clear images and reliable data for various applications. In the cases where beamforming techniques are used, the position of the phase centres need to be known with high accuracy to apply the different techniques and algorithms. Uncertainties in this knowledge will result in high degradation of ambiguity suppression or scanning accuracy.
This PhD thesis aims to develop an on-board method to precisely fine-tune a planar array-type antenna, composed by a set of planar sub-antennas (multiple azimuth channels), using raw SAR data, recovering antenna losses due to surface deformations to a higher degree than antenna coarse calibration methods. Addressing this is essential to meet the demand for high performance SAR together with lighter instruments [1-4, 11].
The overall algorithm behaviour relies on performing on-target low resolution SAR measurements to evaluate the illuminated area shape while intelligently varying local phase elements to compensate the surface deformations. Capitalizing on variations in phase centres will allow to dynamically understand changes in radiation patterns. During on-board processing of a series of SAR sessions the elements phase are adaptively fine tuned to converge to a better performance.
Surface deformations from thermal variations have a slow dynamic, spanning for several minutes, allowing the algorithm sufficient time to adaptively infer and correct the antenna elements phase. The ultimate goal will be to apply the corrections using operation data avoiding as much as possible the need for dedicated calibration campaign.
