Compressive Sampling for Starfield Attitude Estimation
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Solution Overview
Problem
Current star-tracking systems require large amounts of memory and processing power to accurately determine the attitude of an object based on starfield images, which can be costly and power-intensive, especially in radiation-hardened environments.
Innovation Solution
The implementation of compressive sampling techniques to efficiently capture and process starfield image data, reducing memory and processing requirements by storing and processing only compressed data, which allows for more accurate attitude estimation with less power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional star-tracking systems store and process full starfield images, then measurement precision is improved, but memory requirements and processing power increase significantly
Solution Approach 1:
The patent extracts only the essential information from full starfield images by applying compressive sampling techniques. Instead of storing complete images, the system captures compressed representations that retain sufficient detail for attitude estimation while dramatically reducing memory requirements. This extraction principle allows the system to discard redundant data while preserving measurement precision.
Solution Approach 2:
The patent transforms the starfield image data from its original high-dimensional form into a compressed parameter space. By changing the representation parameters through compressive sampling, the system reduces the quantity of data stored while maintaining the essential information needed for accurate attitude determination. This parameter transformation enables efficient storage without sacrificing measurement quality.
2Measurement precision
If traditional star-tracking systems process full starfield images, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The patent extracts only the necessary information from starfield images through compressive sampling, avoiding the computational burden of processing complete images. By working with compressed data representations, the system significantly reduces processing power consumption while maintaining attitude estimation accuracy. This extraction approach eliminates redundant computational operations.
Solution Approach 2:
The patent applies partial action by processing only the essential components of starfield data needed for attitude estimation. Instead of fully processing all image data, the compressive sampling approach captures sufficient information with minimal processing, reducing power consumption while achieving the required measurement precision for spacecraft attitude determination.
3Reliability
If radiation-hardened memory and circuits are used to protect against computational errors, then reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and processes only compressed representations of starfield data, which reduces the amount of data stored in radiation-hardened memory. By minimizing the quantity of stored information through compressive sampling, the system reduces both the complexity and cost of radiation-hardened components while maintaining reliability through error detection and correction on the reduced data set.
4Measurement precision
If more memory and processing power are allocated to star-tracking, then measurement precision is improved, but the system becomes less adaptable to power-constrained environments
Solution Approach 1:
The patent changes the data representation parameters through compressive sampling, transforming full starfield images into compact compressed forms. This parameter change enables the system to achieve high measurement precision with minimal memory and processing resources, making it adaptable to power-constrained environments such as spacecraft where resource allocation must be optimized for multiple competing functions.
Data Source
AI summary
In general, in one embodiment, a starfield image as seen by an object is analyzed. Compressive samples are taken of the starfield image and, in the compressed domain, processed to remove noise. Stars in the starfield image are identified and used to determine an attitude of the object.