Projectile Launcher Shot Monitoring Using Sensor-Based Classification

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Solution Overview

Problem

Existing projectile launcher monitoring systems face challenges in accurately counting shots fired, requiring manual data entry prone to errors, lacking automated customization, and failing to meet market demands for rapid development and personalization, with existing algorithms needing expert intervention and inefficient data labeling.

Innovation Solution

An automated projectile launcher monitoring system that uses a projectile attachment with sensors to collect time-sampled data, applies machine learning to generate a monitoring model, and reduces human intervention by leveraging weak labels, optimizing data collection and shot detection through an iterative process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If manual data entry is used for shot counting, then the system is simple to implement, but human error increases and data accuracy deteriorates

Engineering Contradiction:
Improveease of implementationVSAvoiddata accuracy
Core Design Contradiction:
Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent replaces manual mechanical data entry with an automated electronic shot counting system using sensors (accelerometers, microphones) and signal processing algorithms. The sensor module detects shot events automatically, and the processing module counts and records shots without human intervention, eliminating manual errors while maintaining simplicity through integrated electronics.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If expert intervention is used for algorithm development, then shot detection accuracy is improved, but development time increases and productivity decreases

Engineering Contradiction:
Improveshot detection accuracyVSAvoiddevelopment speed
Core Design Contradiction:
Measurement precisionVSProductivity

Solution Approach 1:

The patent enables the shot counting system to automatically adapt and optimize its own algorithm through machine learning. The processing module uses detected shot patterns to refine counting accuracy over time without requiring external expert intervention for each new weapon type or configuration, thus maintaining high accuracy while accelerating development and deployment.

Inventive Principle:
Principle #25Self-service

3Adaptability or versatility

If customization for personalized projectile launchers is implemented, then adaptability improves, but development time increases and productivity decreases

Engineering Contradiction:
Improvepersonalization capabilityVSAvoiddelivery speed
Core Design Contradiction:
Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements a dynamic, adaptable shot counting system that can be quickly configured for different projectile launcher types through software parameters rather than hardware redesign. The algorithm automatically adjusts to different weapon characteristics (fire rate, recoil patterns, sensor configurations), enabling rapid customization for personalized launchers without extending development cycles.

Inventive Principle:
Principle #15Dynamics

4Extent of automation

If automated shot counting algorithm is generated, then labor time is reduced, but computation time increases and device complexity increases

Engineering Contradiction:
Improveautomation levelVSAvoidalgorithm complexity
Core Design Contradiction:
Extent of automationVSDevice complexity

Solution Approach 1:

The patent divides the shot counting system into distinct functional modules: a sensor module for data acquisition, a processing module for signal analysis and shot detection, and a counting module for tallying events. This segmentation allows each module to be optimized independently, reducing overall system complexity while maintaining high automation. The processing module uses segmented signal processing techniques to analyze complex sensor data efficiently.

Inventive Principle:
Principle #1Segmentation

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

The system provides accurate, automated shot counting and type identification with reduced human effort, enabling rapid customization and meeting market demands for personalized projectile launchers by optimizing data processing and minimizing errors.

Implementation Method 1

The sensor module includes a plurality of sensors such as, but not limited to, acceleration sensors

Methodology Applied
Scientific EffectVibration: Vibration

Implementation Method 2

measuring acceleration of the projectile launcher at a predetermined sample rate

Methodology Applied
Scientific EffectAcceleration: Accelerometer

Data Source

PatentEP4515171B1Automated system and method for a projectile launcher monitoring
Publication Date: 2026.04.08 FN HERSTAL SA
  • EP4515171B1 patent drawingFigure 1~2
  • EP4515171B1 patent drawingFigure 3
  • EP4515171B1 patent drawingFigure 4

AI summary

The present invention relates to an automated system and method for a projectile launcher monitoring. The automated projectile launcher monitoring system comprises a projectile attachment and a processing module. The projectile attachment coupled with a projectile launcher includes a sensor module and a communication module. The projectile attachment acquires signals from the sensor module and converts them into a time-sampled data. The communication module sends the time-sampled data to the processing module corresponding to a shooting plan. The processing module is coupled with the projectile attachment and configured process the time-sampled data to generate a monitoring model. The processing module produces at least one-shot candidate from the time-sampled data when at least one metric is satisfied, and deploys an instance-level classifier to categorize the at least one shot candidate to generate at least one prediction. The processing module further computes an estimated proportion by aggregating the at least one prediction, compares the estimated proportion with a real proportion to determine a loss function, and generates the monitoring model through an iterative process until a predetermined minimal loss is achieved.