Optical Correlation for Point Source Detection
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Solution Overview
Problem
Differentiating point source energy signatures from extended sources in threat detection systems is challenging due to optical blurring, which often results in false detections and inaccurate identification of threats, especially when point sources are smaller than a single pixel and emit energy across multiple pixels.
Innovation Solution
A method and system that map subpixel blur of a point source through camera optics onto a focal plane array, calculating and storing ideal energy values for sub-pixel locations, and comparing observed energy signatures against these values to determine correlation with a point source, using a center of mass calculation and linear best-fit curve analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optical sensors are used to detect point source energy signatures, then threat detection capability is improved, but optical blurring causes false detections and reduces measurement precision
Solution Approach 1:
The patent divides each pixel into multiple sub-pixel regions to capture the blurred point source energy distribution. By segmenting the pixel area and analyzing the energy signature across sub-pixel locations, the system can distinguish point sources from extended sources even when optical blurring causes the point source to span multiple pixels. This segmentation approach maintains threat detection reliability while improving measurement precision through detailed spatial energy analysis.
2Area of stationary object
If point sources are detected across multiple pixels due to blurring, then detection coverage is improved, but differentiation from extended sources becomes difficult reducing detection accuracy
Solution Approach 1:
The patent applies local quality analysis by examining the specific energy distribution pattern within each sub-pixel region. Point sources exhibit a characteristic concentrated energy pattern even when blurred, whereas extended sources show distributed energy patterns. By analyzing the local energy quality at sub-pixel locations and comparing against ideal point source templates, the system can accurately differentiate point sources from extended sources across the entire detection coverage area.
Solution Approach 2:
Instead of trying to prevent blurring or resolve it to a single pixel, the patent inverts the approach by embracing the blurred distribution and using it as a signature. The system captures the expected blurred point source pattern through ideal template creation and compares observed energy distributions against these templates. This inversion transforms the blurring problem into a solution, enabling accurate point source identification across multiple pixels.
3Measurement precision
If ideal energy values are calculated and stored for correlation analysis, then point source identification accuracy is improved, but system complexity and data storage requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing ideal point source energy values for various sub-pixel locations before actual threat detection. These ideal templates represent the expected blurred point source signatures at different positions within pixels. During operation, the system simply compares observed energy distributions against these pre-computed templates, significantly reducing real-time processing complexity while maintaining high identification accuracy.
Data Source
AI summary
A method of determining the point source quality of a set of pixels associated with a detected energy signature is discussed that pre-records ideal test point source signatures at various sub-pixel locations and radiant intensities throughout the overall sensor field of view in a focal plane array, determines the sub-pixel location of an observed source, and compares the signature at a pixel of the observed source to the pre-recorded “ideal source” signatures at the determined sub-pixel location. to determine point source correlation.