Digital Weapon Sight Aimpoint Correction Using Image Registration
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Solution Overview
Problem
Current digital weapon sights lack a fast, hands-free method for accurately aligning the weapon sight and weapon, which is essential for precise aiming, especially in varying conditions, due to the complexity of existing image processing algorithms and resource limitations in small processors.
Innovation Solution
A method employing digital image registration techniques to automatically adjust the on-screen reticle by capturing and analyzing pre-shot and post-shot imagery, using cross-correlation algorithms and lightweight image processing methods to determine the misalignment and correct it, minimizing user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image processing algorithms (e.g., template matching, SURF method, affine transformations) are used to determine aimpoint correction, then measurement precision of the point of impact is improved, but device complexity increases and cannot be implemented in small processors
Solution Approach 1:
The patent extracts only the essential features needed for aimpoint correction from the full image processing pipeline. Instead of using complex algorithms like SURF or template matching, it isolates and processes only the projectile trajectory and impact point data, discarding unnecessary computational steps while maintaining measurement precision.
Solution Approach 2:
The patent inverts the traditional approach by not trying to make the complex algorithms work on resource-constrained devices, but rather designing a simplified algorithm specifically optimized for small processors and FPGAs. The solution is built from the ground up to match the computational capabilities of the target hardware rather than attempting to reduce complexity of existing complex solutions.
2Measurement precision
If manual zeroing procedures are used to align weapon sight and weapon, then aimpoint correction can be achieved, but loss of time increases and ease of operation decreases
Solution Approach 1:
The system performs automatic aimpoint correction without requiring manual user intervention. The processor automatically captures images, detects the point of impact, calculates the correction needed, and adjusts the reticle position, eliminating the need for manual zeroing procedures and significantly reducing the time required.
Solution Approach 2:
The system implements a feedback loop where the actual point of impact is detected and compared with the intended aim point, and the reticle is automatically adjusted based on this feedback. This closed-loop approach ensures accurate alignment while minimizing user involvement and time expenditure.
3Use of energy by moving object
If digital weapon sights use low-power processors and small memories, then power consumption is reduced and device portability is improved, but device complexity increases due to resource constraints
Solution Approach 1:
The patent changes the parameters of the image processing algorithm to match the computational capabilities of low-power processors. By optimizing the algorithm's computational requirements and using efficient data structures, the system achieves accurate aimpoint correction on resource-constrained hardware without requiring high-power processors or large memories.
Data Source
AI summary
Systems, devices, and methods are disclosed to correct aimpoint of a system of a weapon and affixed digital weapon sight. The method employs the components of a digital weapon sight configured together for a process of sensing of a ballistic event, storing of digital imagery data before and after a shot or series of shots, and then the performing of an image registration algorithm, via a cross-correlation type function. The image registration determines the misalignment between the images as a Cartesian shift, and aimpoint of subsequent shooting is corrected through application of adjusted reticle symbology or adjusted image sensor windowing. Preprocessing methods for enhancing image registration algorithm accuracy are presented.