Star Map Grid Matching Under Noise and Pseudo Stars
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Solution Overview
Problem
Existing star map identification algorithms in star sensors are prone to errors due to noise interference, pseudo stars, and limited positioning accuracy, especially when identifying weak targets, leading to incorrect pattern recognition and significant time consumption.
Innovation Solution
A star map identification method using a composite calibration star grid pattern that involves selecting multiple neighboring stars as calibration stars, constructing grid pattern features, and applying a field of view constraint to verify the initial matching results, thereby improving pattern stability and reducing noise influence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pattern recognition algorithms are used for star map identification, then identification capability is improved, but accuracy deteriorates due to noise and pseudo stars
Solution Approach 1:
The patent divides the star map identification process into multiple independent modules: calibration star selection, grid pattern construction, pattern matching, and result verification. Each module processes specific aspects of the identification task, allowing noise and pseudo stars to be handled systematically at different stages without compromising overall accuracy.
Solution Approach 2:
The patent performs preliminary calibration star selection and grid pattern construction before the actual pattern matching. By pre-establishing the grid pattern based on calibration stars and pre-calculating reference values, the system prepares all necessary templates and parameters in advance, enabling accurate and efficient identification even in noisy conditions.
2Difficulty of detecting and measuring
If radial pattern algorithms are used, then neighboring star distribution is captured, but representation completeness deteriorates
Solution Approach 1:
The patent extracts the grid pattern information from the complex star distribution data and represents it as a simplified binary grid matrix. By transforming the continuous star distribution into discrete grid cells (0 or 1), the system captures essential spatial relationships while filtering out redundant information, achieving both simplicity and completeness.
Solution Approach 2:
The patent applies different processing strategies to different regions of the star map. The grid pattern construction focuses on local neighborhoods around calibration stars, while the global pattern matching considers the overall distribution. This localized approach ensures complete representation of neighboring stars without overwhelming computational complexity.
3Extent of automation
If circumferential pattern algorithms are used, then pattern recognition is improved, but reliability deteriorates due to noise disturbance
Solution Approach 1:
The patent prepares multiple backup calibration stars and grid patterns in advance. When noise or pseudo stars interfere with the primary pattern recognition, the system can switch to alternative calibration stars or grid configurations that were pre-prepared, cushioning against the impact of noise and maintaining reliable operation.
4Device complexity
If triangle algorithm is used for subgraph isomorphism, then algorithm simplicity is improved, but positioning accuracy deteriorates
Solution Approach 1:
The patent transitions from two-dimensional star coordinate matching to a multi-dimensional feature space that includes grid pattern characteristics, brightness information, and spatial relationships. By elevating the matching process to higher dimensions, the system achieves both algorithmic simplicity and high positioning accuracy through comprehensive feature utilization.
5Extent of automation
If pyramid algorithm is used for subgraph isomorphism, then feature subgraph construction is improved, but time consumption increases significantly
Solution Approach 1:
The patent performs partial feature subgraph construction by focusing only on the most critical calibration stars and their immediate neighborhoods rather than processing all stars in the entire star map. This selective approach maintains sufficient identification capability while dramatically reducing computational time and resources required.
6Extent of automation
If group matching method is used, then identification process is improved, but accuracy deteriorates due to brightness and noise limitations
Solution Approach 1:
The patent dynamically adjusts the weightings and thresholds of various identification parameters based on the specific characteristics of the observed star map. By adapting brightness thresholds, grid resolution, and matching criteria in real-time, the system maintains high accuracy despite variations in noise levels and stellar brightness distributions.
Data Source
AI summary
A star map identification method based on a composite calibration star grid pattern is provided, which includes obtaining an observation star map, selecting a host star to be identified based on the observation star map; constructing the grid pattern features of the calibration star and performing comparison of a lookup table to obtain candidate matching results; obtaining candidate matching stars corresponding to the host star to be identified, carrying out validity verification of the initial matching results based on the FOV constraint, and filtering the candidate matching results through the calculation of angular distance and voting. The method based on the composite calibration star grid pattern uses the grid pattern as the matching pattern feature, by introducing multiple calibration star measurements, the stability of the characteristic pattern in construction is improved, and the influence of position noise and brightness noise inn the algorithm recognition process is reduced.