Star Tracker ROI Subsampling for Daylight Celestial Detection
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Solution Overview
Problem
Conventional star trackers struggle to accurately detect celestial objects during daylight due to increased and non-uniform background light from sunlight scattering, leading to diminished signal-to-noise ratio and inability to correct navigation errors in inertial sensors.
Innovation Solution
A method and apparatus that divide the field of view into sub-regions of interest (sub-ROIs), identify candidate celestial objects within these regions, and determine their angular velocities to distinguish known celestial objects from background noise, enabling accurate navigation during daylight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional background subtraction algorithms are used during daylight, then processing is simplified, but signal-to-noise ratio remains insufficient due to non-uniform background light
Solution Approach 1:
The patent divides the region of interest into multiple sub-regions (N sub-ROIs) and processes each sub-region separately. This segmentation allows the system to handle non-uniform background light by treating each sub-region independently, identifying candidate celestial objects in each sub-region, and determining their angular velocities separately. This resolves the contradiction by maintaining manageable processing complexity while significantly improving signal-to-noise ratio through localized analysis.
Solution Approach 2:
The patent introduces temporal dynamics by tracking angular velocity of candidate celestial objects across multiple time periods. By determining angular velocity during a first time period and verifying it matches expected values during a second time period, the system dynamically filters out non-celestial objects and background noise. This dynamic approach improves measurement precision without requiring overly complex static processing algorithms.
2Adaptability or versatility
If star tracker operates during daylight, then navigation capability is extended, but detection accuracy deteriorates due to scattered sunlight
Solution Approach 1:
By segmenting the field of view into multiple sub-ROIs, the patent enables daylight operation while maintaining detection accuracy. Each sub-region is processed independently to identify candidate celestial objects, and the segmentation reduces the impact of scattered sunlight by localizing the analysis area. This allows the star tracker to operate during both nighttime and daytime with acceptable accuracy.
Solution Approach 2:
The patent employs dynamic temporal verification by measuring angular velocity across multiple time periods. Candidate objects detected during daylight are verified by checking if their angular velocity during a second time period matches the expected angular velocity from a first time period. This dynamic verification process maintains detection accuracy despite the challenging daylight conditions, enabling extended operational versatility.
3Productivity
If region of interest is processed as a whole, then processing is efficient, but candidate celestial objects cannot be reliably distinguished from background noise
Solution Approach 1:
The patent divides the region of interest into N sub-ROIs and processes each sub-region separately to identify candidate celestial objects. This segmentation improves reliability by reducing false positives from background noise, as each sub-region is analyzed independently. The modular approach maintains reasonable processing efficiency by allowing parallel processing of multiple sub-regions while achieving more reliable object identification than whole-region processing.
Solution Approach 2:
The patent introduces temporal dynamics by verifying candidate objects across multiple time periods through angular velocity measurement. Candidates identified in sub-ROIs during a first time period are verified during a second time period by checking if their angular velocity matches expected values. This dynamic verification significantly improves object identification reliability while maintaining processing efficiency through the structured multi-step approach.
Data Source
AI summary
Techniques are disclosed for improving daylight performance of a star tracker. A region of interest (ROI) within a field of view of sky is obtained. The region of interest is divided into sub-ROIs. Sub-ROIs including a candidate celestial object are identified. An angular velocity of each candidate celestial object is determined. The known celestial object is a candidate celestial object identified as having a angular velocity, during a time period, that is within a range of angular velocities about a known angular velocity associated with the known celestial object.