Digital Weapon Sight Aimpoint Correction Using Image Registration
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Solution Overview
Problem
Existing digital weapon sights lack a fast, hands-free method for aligning the sight and weapon, and existing algorithms are too complex for small processors or require high power and memory resources.
Innovation Solution
A digital weapon sight system that captures and analyzes pre-shot and post-shot imagery data to automatically adjust the reticle position using image registration algorithms, allowing for quick and reliable alignment with minimal user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image analysis algorithms (SURF, affine transformations) are used to determine aimpoint, then measurement precision is improved, but device complexity and processing requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for aimpoint determination - the bullet hole location and its relationship to the reticle - while discarding unnecessary complex processing steps. The system captures pre-shot and post-shot images, identifies the bullet hole position through simple image comparison, and calculates the required reticle adjustment without employing complex algorithms like SURF or affine transformations.
Solution Approach 2:
The patent uses simple, readily available image processing techniques rather than complex, resource-intensive algorithms. The system employs basic image registration and coordinate transformation methods that can be implemented on low-power processors, making the solution practical for consumer-grade digital weapon sights without requiring expensive computational resources.
2Reliability
If manual zeroing procedure is used, then reliability is improved through careful adjustment, but loss of time increases due to multiple steps required
Solution Approach 1:
The system performs automatic zeroing by capturing images before and after firing, automatically identifying the bullet hole position relative to the reticle, and calculating the required adjustment. This eliminates the need for manual measurement and adjustment steps, allowing the system to self-calibrate without requiring the user to perform time-consuming manual procedures while maintaining reliable aiming accuracy.
Solution Approach 2:
The system captures post-shot images showing where the bullet actually impacted relative to the reticle center, uses this feedback information to automatically calculate the error, and adjusts the reticle position accordingly. This closed-loop feedback mechanism enables rapid automatic zeroing that is both reliable and time-efficient, eliminating the iterative manual adjustment process.
3Ease of operation
If hands-free alignment method is implemented, then ease of operation is improved, but measurement precision may be compromised without careful verification
Solution Approach 1:
The patent replaces manual mechanical adjustment with automated electronic image processing. Instead of requiring the user to physically adjust the reticle and verify alignment through trial shots, the system automatically captures images, processes them to determine the bullet hole position relative to the reticle, and calculates the precise adjustment needed. This substitution maintains or improves precision while dramatically enhancing ease of operation.
Solution Approach 2:
The system creates digital copies of the pre-shot and post-shot images, analyzes these copies to determine the relationship between the reticle and bullet impact point, and uses this information to calculate the required adjustment. This copying and analysis approach enables hands-free automatic alignment while maintaining measurement precision through accurate digital image processing.
Data Source
AI summary
Systems and methods are disclosed to correct aimpoint of 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 orientation data and digital imagery data before and after a shot or series of shots, and then performing an image registration algorithm using the imagery data stored. The image registration determines the translational offset between the images, and aimpoint of subsequent shooting is corrected through application of adjusted reticle symbology or adjusted image sensor windowing.