Matched Filter Tracking for Faint Space Object Detection
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Solution Overview
Problem
Existing technologies fail to efficiently detect and track small asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft, the computational load in moving asteroids or relic spacecraft.
Innovation Solution
A multiple telescope imaging system with matched filter tracking, utilizing a digital focal plane and image processing system to reduce computational load by optimizing bin size, tripwire placement, and exposure time for real-time detection and tracking of faint objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If matched filter tracking is performed on all pixels in captured images to detect faint moving objects, then detection precision is improved, but computational load increases significantly
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) based on detected stationary objects like stars. Only pixels within these ROIs are processed for moving object detection, while the rest of the image is excluded from computational analysis. This segmentation approach maintains detection precision within relevant areas while significantly reducing overall computational load.
Solution Approach 2:
Different processing quality is applied to different parts of the image. Regions containing stationary objects receive full processing attention for accurate ROI definition, while regions outside ROIs receive no processing. This local quality approach optimizes computational resource allocation to where it is most needed for detecting faint moving objects.
2Loss of time
If real-time detection and tracking is implemented for faint moving objects, then timeliness of collision warning is improved, but processing speed requirements increase
Solution Approach 1:
Stationary objects are detected and used to define regions of interest before the actual moving object detection process. This preliminary action of establishing ROIs based on stationary references prepares the system in advance, allowing subsequent moving object detection to proceed efficiently in real-time without unnecessary computational delays.
Solution Approach 2:
By segmenting the image into multiple ROIs based on stationary object positions, the system processes only relevant regions in real-time. This segmentation enables parallel processing of multiple small regions rather than one large region, improving processing speed while maintaining real-time detection capability for collision warnings.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time detection and tracking of moving asteroids or relic spacecraft.
Implementation Method 1
a digital focal plane including an optically sensitive array configured to capture imagery data by transducing light entering through the optical system into digital data
Data Source
AI summary
Systems and techniques for the optimized matched filter tracking of space objects are provided. In one aspect, a method of detecting faint objects includes receiving a plurality of telescope images from a telescope imaging system and performing a plurality of computational calculations on the telescope images using a plurality of search parameters to identify one or more objects moving through the telescope images. The number of the computational calculations is reduced by a priori relatively restricting a parameter space of the computational calculations.