Satellite Sensor Intercalibration via Affine Transformations
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Solution Overview
Problem
Satellite constellations experience performance degradation due to miscalibration of sensors, leading to inconsistencies in measurements across different satellites.
Innovation Solution
A system and method for intercalibration between remote sensing systems, which involves collecting measured information from multiple space vehicles and a reference vehicle, deriving affine systems, calculating physical distance metrics, identifying closely coincident vehicles, performing similarity analysis to derive intercalibration parameters, and applying these parameters to produce calibrated sensor information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If satellite constellations use multiple sensors for measurements, then the quantity and coverage of data are improved, but measurement precision deteriorates due to miscalibration differences between sensors
Solution Approach 1:
The patent applies parameter changes by transforming sensor measurements through affine transformations (gain and offset adjustments) to normalize data from different sensors. The system derives intercalibration parameters that modify the measurement parameters of individual sensors to achieve consistency with reference sensor measurements, thereby resolving the precision deterioration caused by miscalibration while preserving the quantity advantage of multiple sensors.
Solution Approach 2:
The patent implements feedback by using reference sensor measurements to evaluate and adjust the calibration parameters of other sensors in the constellation. The system continuously compares measurements from multiple sensors against reference measurements, derives intercalibration parameters based on these comparisons, and applies corrections to maintain measurement consistency, creating a closed-loop calibration system.
2Measurement precision
If intercalibration is performed for all space vehicles, then measurement consistency is improved, but device complexity increases due to the need for comprehensive similarity analysis
Solution Approach 1:
The patent applies segmentation by dividing the intercalibration process into distinct stages: selecting reference space vehicles, deriving affine systems for individual vehicles, calculating similarity metrics, and applying intercalibration parameters. This segmented approach breaks down the complex task of calibrating entire satellite constellations into manageable, systematic steps that can be processed independently and combined.
Solution Approach 2:
The patent implements partial action by selectively applying intercalibration only to space vehicles that meet specific similarity criteria (below a threshold distance metric). Rather than performing comprehensive calibration on all vehicles in the constellation, the system identifies and processes only those vehicles sufficiently similar to reference vehicles, reducing computational complexity while maintaining measurement consistency for the relevant subset.
3Measurement precision
If similarity analysis is performed on all space vehicles, then intercalibration accuracy is improved, but loss of time increases due to extensive processing requirements
Solution Approach 1:
The patent applies partial action by performing similarity analysis only on space vehicles that fall within a predefined threshold distance from reference vehicles. This selective approach filters out vehicles that are too dissimilar to benefit from intercalibration, significantly reducing the number of vehicles requiring detailed similarity analysis and thus minimizing processing time while maintaining accuracy for the relevant subset.
Solution Approach 2:
The patent implements preliminary action by pre-calculating and storing affine systems for each space vehicle before performing similarity analysis. This preliminary derivation of transformation parameters allows the system to quickly evaluate similarity metrics without performing full calibration computations on all vehicles, reducing processing time while preserving the ability to achieve accurate intercalibration when needed.
Data Source
AI summary
Systems and methods are provided for updating data in a computer network. An exemplary method includes: collecting a first set of measured information from a plurality of space vehicles and a second set of measured information from a reference space vehicle; deriving an affine system for each of the space vehicles; deriving a set of physical distance metrics between each of the space vehicles and the reference space vehicle; identifying a set of space vehicles having a physical distance metric below a threshold; upon identifying the set of space vehicles, performing a similarity analysis for each of the identified set of space vehicles producing a set of intercalibration parameters; applying the set of intercalibrations parameters to the affine system of each of the identified set of space vehicles; and forming an ensemble product defining calibrated sensor information from the space vehicles.