Projectile Launcher Configuration for Passive Drone Interception
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The emergence of small, agile drones with low radar cross section and low signal-to-noise ratio poses a challenge for Counter Unmanned-Aircraft Systems (C-UAS), as existing high-cost defense systems are economically inefficient against these threats.
Innovation Solution
A low-cost system using a passive sensor and a computing device to determine the configuration of a projectile launching device, such as a machine gun, to accurately hit moving targets by combining passive sensing data with ballistic modeling and predictive control algorithms to compensate for drag, drop, and atmospheric conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-cost defence systems such as air defence missiles are used to counter new weapons, then the detection and neutralization capability is improved, but the economic efficiency deteriorates due to the asymmetric cost between defence systems (hundreds of thousands of Euros) and drones (tens of thousands of Euros)
Solution Approach 1:
The patent employs low-cost passive sensors (electrooptical cameras) and inexpensive countermeasures instead of expensive active radar systems and high-cost missiles. The system uses disposable computational resources for ballistic modeling and prediction, replacing expensive hardware with software-based solutions that achieve comparable or superior effectiveness at fraction of the cost.
Solution Approach 2:
The patent substitutes expensive mechanical radar systems with passive electrooptical sensors, and replaces complex active detection systems with computational approaches (ballistic modeling, dead reckoning, AI-based prediction). This substitution of physical systems with computational methods achieves cost reduction while maintaining or improving detection and tracking capability.
2Ease of manufacture
If passive sensors are used to detect drones with low radar cross section and low signal-to-noise ratio, then the cost is reduced, but the measurement precision deteriorates due to difficulty in detecting small agile objects
Solution Approach 1:
The patent performs preliminary computational actions including ballistic modeling of projectile trajectories, dead reckoning to predict object positions, and AI-based behavior prediction before the actual detection and engagement. This preliminary computational preparation compensates for the lower precision of passive sensors by predicting where the target will be and how to intercept it.
Solution Approach 2:
The system continuously updates ballistic models and predictions based on new sensor data, creating a feedback loop that refines measurement precision over time. The computational model adapts to the target's actual behavior, compensating for initial detection uncertainties and improving tracking precision as the engagement progresses.
3Reliability
If predictive algorithms are used to determine future location of detected objects, then the ability to hit moving targets is improved, but the device complexity increases due to computational requirements
Solution Approach 1:
The patent segments the complex problem of predicting and hitting moving targets into separate computational modules: ballistic modeling for projectile trajectories, dead reckoning for position prediction, AI-based behavior analysis for maneuver prediction, and configuration determination for the launching device. This segmentation allows each module to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The computational system serves itself by using the same hardware platform (general-purpose processors, GPUs, or FPGAs) for both running the complex predictive algorithms and controlling the launching device. The system leverages modern computational resources efficiently, using algorithms that can execute on commodity hardware rather than requiring specialized expensive equipment.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
According to one aspect of the present disclosure, there is provided a computer-implemented method of determining a configuration of a projectile launching device, the method includes receiving first data from a passive sensor; determining a first location of a detected object based on the first data; determining a second location based on the first location; and determining a configuration of the projectile launching device to propel a projectile to collide with the detected object in the second location.