Jan 13, 2026

US12523438 - Automated system and method for a projectile launcher monitoring

An automated projectile launcher monitoring system includes 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 time-sampled data, which is sent to the processing module corresponding to a shooting plan. The processing module is coupled with the projectile attachment and configured to process the time-sampled data to generate a monitoring model. The processing module produces shot candidate(s) from the time-sampled data when at least one metric is satisfied, and deploys an instance-level classifier to categorize the shot candidate(s) to generate prediction(s). The processing module further computes an estimated proportion by aggregating the prediction(s), 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.

Assignee: FN HERSTAL S.A.
Inventors: Nathan Morsa
Filing date: 2023-04-24

The patent describes an automated monitoring system for projectile launchers that utilizes a sensor module to collect data, which is then processed to generate a monitoring model. This system employs an iterative process to optimize predictions about shot candidates based on real-time data and predefined metrics.

Claim 1

1 . An automated system ( 300 ) for monitoring a projectile launcher, the automated system comprising: a sensor module ( 202 ), configured to be coupled with the projectile launcher to acquire signals and convert the signals into time-sampled data; a communication module ( 226 ) configured to receive a shooting plan ( 404 ) and send the time-sampled data corresponding to the shooting plan ( 404 ) using the sensor module ( 202 ); and a processing module ( 302 , 412 ) configured to receive and process the time-sampled data from the communication module ( 226 ) to generate a monitoring model ( 434 ), wherein the processing module ( 302 , 412 ) is further configured to: produce at least one shot candidate ( 420 ) from the time-sampled data when at least one metric ( 418 ) is satisfied, deploy an instance-level classifier ( 322 , 422 ) to categorize the at least one shot candidate ( 420 ) to generate at least one prediction ( 424 ), compute an estimated proportion ( 426 ) by aggregating the at least one prediction ( 424 ), compare the estimated proportion ( 426 ) with a real proportion ( 428 ) to determine a loss function ( 430 ), and generate the monitoring model ( 434 ) through an iterative process until a predetermined minimal loss is achieved. a sensor module ( 202 ), configured to be coupled with the projectile launcher to acquire signals and convert the signals into time-sampled data; a communication module ( 226 ) configured to receive a shooting plan ( 404 ) and send the time-sampled data corresponding to the shooting plan ( 404 ) using the sensor module ( 202 ); and a processing module ( 302 , 412 ) configured to receive and process the time-sampled data from the communication module ( 226 ) to generate a monitoring model ( 434 ), wherein the processing module ( 302 , 412 ) is further configured to: produce at least one shot candidate ( 420 ) from the time-sampled data when at least one metric ( 418 ) is satisfied, deploy an instance-level classifier ( 322 , 422 ) to categorize the at least one shot candidate ( 420 ) to generate at least one prediction ( 424 ), compute an estimated proportion ( 426 ) by aggregating the at least one prediction ( 424 ), compare the estimated proportion ( 426 ) with a real proportion ( 428 ) to determine a loss function ( 430 ), and generate the monitoring model ( 434 ) through an iterative process until a predetermined minimal loss is achieved. produce at least one shot candidate ( 420 ) from the time-sampled data when at least one metric ( 418 ) is satisfied, deploy an instance-level classifier ( 322 , 422 ) to categorize the at least one shot candidate ( 420 ) to generate at least one prediction ( 424 ), compute an estimated proportion ( 426 ) by aggregating the at least one prediction ( 424 ), compare the estimated proportion ( 426 ) with a real proportion ( 428 ) to determine a loss function ( 430 ), and generate the monitoring model ( 434 ) through an iterative process until a predetermined minimal loss is achieved.

Google Patents

https://patents.google.com/patent/US12523438

USPTO PDF

https://image-ppubs.uspto.gov/dirsearch-public/print/downloadPdf/12523438