Shooter Identification via Image Analysis for Live Fire Training
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Solution Overview
Problem
Live fire combat training lacks individual shooter performance tracking and collaborative training capabilities, making it an undocumented and inefficient experience, especially when conducted with multiple shooters in different geographic locations.
Innovation Solution
A system comprising shooter-side and target-side sensors, along with a processing unit, to capture and analyze images of shooters and projectile strikes, uniquely identify shooters, and correlate discharges with strikes, enabling real-time performance data and collaborative training across distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If live fire ammunition is used for combat training, then training realism and effectiveness are improved, but individual shooter performance tracking capability deteriorates
Solution Approach 1:
The patent introduces an intermediary identification system that uses image sensors and processing units to detect and track shooters' actions. This intermediary system bridges the gap between live fire training and performance documentation, allowing realistic live fire exercises to be correlated with digital performance data without requiring shooters to wear detection devices.
Solution Approach 2:
The patent creates a digital copy of the live fire training event by capturing images of shooters and targets, then processing these images to generate performance data. This copying mechanism allows the physical live fire training to be replicated and analyzed digitally, preserving performance information without interfering with the realism of the actual training.
2Loss of information
If body mounted detection mechanisms are provided to shooters, then individual performance tracking is improved, but ease of operation deteriorates
Solution Approach 1:
Instead of equipping shooters with detection devices (the conventional approach), the patent inverts the approach by placing image sensors and processing equipment at fixed locations to capture and identify shooters passively. This reversal eliminates the burden on shooters while still achieving performance tracking through automated image analysis.
3Adaptability or versatility
If multiple shooters train simultaneously at different locations, then collaborative training capability is improved, but system complexity deteriorates
Solution Approach 1:
The patent creates a universal training system where the same image capture and processing infrastructure can support multiple shooters at different locations simultaneously. The system processes images from multiple sources through a common identification algorithm, allowing collaborative training without requiring location-specific customization of the core system.
Data Source
AI summary
At least one shooter-side image sensor captures images of a plurality of shooters and a plurality of respective firearms periodically fired by the shooters. At least one target-side sensor collects data indicative of projectile strikes on a target area associated with at least one target. A processing unit analyzes images captured by the shooter-side image sensor and detects projectile discharges in response to firing of the firearms, and uniquely identifies each of the shooters associated with the detected projectile discharges. The processing unit detections of projectile strikes, based on the data collected by the target-side sensor, and the detected projectile discharges and identifies, for each detected projectile strike on the target area, the correspondingly fired firearm associated with the uniquely identified shooter.