Tenders are invited for Automated Phased Array Factory Calibration from Radiation Pattern Using Artificial Intelligence (Artes at 7b.082) Expro+ The objective of this activity is to develop automated factory calibration of phased arrays to improve the manufacturing process for satellite user terminals in the context of LEO constellations leveraging Artificial Intelligence and Machine Learning techniquesTargeted Improvements: - Enable factory calibration (less than 1 second) from single-shot radiation pattern measurements of phased arrays. - Error between post-factory calibration theoretical should be below 0.5dBDescription:Traditionally, satellite manufacturing was focused on producing a limited number of bespoke satellites for GEO orbits. However, the rise of LEO constellations demandsa shift towards mass production, requiring efficient assembly and testing processes. This statement extends beyond satellite payloads to LEO user terminals, where phased arrays are becoming ubiquitous for constant asset tracking. With satellite user terminal systems increasingly reliant on phased arrays, precise calibration becomes crucial for optimal performance. Calibration involves ensuring precise alignment and amplitude matching of each element to achieve optimal beamforming, thereby maximising signal strength, and minimising interference. Still, current manual calibration methods are mostly labour-intensive, time-consuming, and prone to errors, presenting a challenge to the efficiency goals of mass manufacturing. To overcome this, there is a need to develop AI/ML solutions capable of extracting calibration insights from single-scan radiation patterns measurements. Previous research has demonstrated the potential of AI/ML techniques in synthesising radiation patterns, identifying faulty elements and predicting unmeasured phased values speeding up calibration of antenna arrays. Building upon these advancements, this activity will streamline the calibration process of phased arrays using AI/ML techniques. Rather than using brute force or iterative methods, the objective is to achieve fast calibration and accurate in a single shot. Additionally, the AI/ML engine will be capable to cope with process, voltage, and temperature variations inherent to the integrated circuits used in phased arrays, ensuring robust calibration across manufacturing batches. This approach not only addresses the immediate challenge of mass-producing user terminals but also lays the foundation for adaptabletesting systems capable of accommodating variations across manufacturing batches. By transitioning from labour-intensive manual methods to streamlined single-shot calibration, manufacturers can achieve greater efficiency and accuracy in production. The developed calibration process will be implemented in a combined software and hardware test-bed that will be developed, and the performance ofthe developed calibration process will be tested.Read more Tender Link : https://esastar-publication-ext.sso.esa.int/ESATenderActions/filter/open
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