Subpixel Correlation for Time To Go Estimation
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Solution Overview
Problem
Existing methods for calculating Time To Go (TTG) between a vehicle and an intruding object in UAVs suffer from high uncertainty, affecting the accuracy of collision avoidance systems.
Innovation Solution
A method involving image filtering, sub-pixel correlation analysis, and scale factor calculation to accurately determine TTG by comparing consecutive images from electro-optical sensors, enhancing edge detection and reducing absolute signal energy to improve correlation values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time to go is estimated based on scale change between target points in consecutive images, then the collision avoidance system can detect intruding objects, but the uncertainty in time to go estimates becomes high
Solution Approach 1:
The patent applies preliminary action by performing image filtering and enhancement operations before the correlation analysis. The images are filtered to remove noise and enhance edges prior to comparing scale changes, which prepares the data in advance to improve measurement precision and reduce uncertainty in the time to go estimates
Solution Approach 2:
The patent uses copying by creating multiple scaled versions of the second image with different scale factors. These copied and scaled images are then correlated with the first image to find the best match, which improves the precision of scale change measurement and thereby reduces uncertainty in time to go estimation
2Reliability
If tracking filters are used to estimate time to go from radar observations, then the system can track nearby aircraft, but the uncertainty in time to go estimates becomes high
Solution Approach 1:
The patent replaces the mechanical tracking filter approach with an image processing-based correlation method. Instead of using radar observations processed through tracking filters, the system uses electro-optical sensor images that are filtered, scaled, and correlated to determine time to go, which provides better measurement precision and reduces uncertainty
3Measurement precision
If image filtering is performed to enhance edges and remove absolute signal energy dependence, then correlation values between images improve, but the processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct stages: first filtering to remove absolute signal energy dependence, then enhancing edges, and finally performing correlation analysis. This segmented approach improves measurement precision at each stage while managing processing complexity through systematic breakdown of operations
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
This approach provides a highly accurate TTG value and size estimation of intruding objects, enhancing the reliability of collision avoidance maneuvers in UAVs.
Implementation Method 1
retrieving a first image of said intruding object at a first point of time, T0, and a second image of said intruding object at a second point of time, T1
Data Source
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AI summary
The present invention provides a method and a system for calculating a Time To Go, TTG, value between a vehicle and an intruding object. The method comprises the steps of: first retrieving (S43) a first image of the intruding object at a first point of time, To, and a second image of the intruding object at a second point of time, T1. Next filtering (S44) the first image and the second image so that the first image and the second image become independent of absolute signal energy and so that edges become enhanced. In a next step (S45) an X fractional pixel position, XFRAC, and a Y fractional pixel position, YFRAC, are set to zero, where XFRAC denotes a horizontal displacement at sub pixel level and YFRAC a vertical displacement at sub pixel level. In the step (S45) a scale factor, Si, is also selected. The second image is then scaled in a step (S46) with the scale factor, Si, and resampled to position XFRAC and YFRAC, which results in a resampled scaled image, RSiI Next correlation values, CXPIX, YPIX, i, are calculated in a step (S47) between the first image and the resampled scaled image, RSiI, for different horizontal displacements at pixel level, XPIX, and different vertical displacements at pixel level, YPIX, for the resampled scaled image RSiI. Thereafter a maximum correlation value at subpixel level, Ci, is found in a step (S48) based on the correlation values, CXPIX, YPIX i. In step (S48) XFRAC and YFRAC are also updated. In a next step (S49) j is set to j = j+1 and the steps (S46) to (S49) are repeated a predetermined number of times. Thereafter i is set to i = i + 1 and the steps (S45) to (S50) are repeated a predetermined number of times. In a next step (S51) a largest maximum correlation value, CMAX, is found among the maximum correlation values, Ci, and the scale factor Si, MAX associated with the largest maximum correlation value CMAX- In a step (S52) the Time To Go, TTG, is calculated based on the scale factor Si, MAX