sUAS Deterrent Using Multi-Sensor Fusion and ML Threat Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current countermeasure systems for small unmanned aerial systems (sUAS) are inadequate for civilian environments, as they often cause collateral damage and are not designed to detect slow-moving targets at low altitudes, and existing military systems are not suitable for commercial use due to size, cost, and regulatory issues.
Innovation Solution
An integrated multi-sensor system that combines radar, RF direction finding, electro-optical/infrared cameras, and laser range finders to detect, track, classify, and deter sUAS with precision, using machine learning and data mining for threat assessment and tailored RF countermeasures to disrupt control systems without affecting civilian GPS signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If military countermeasure systems are used against sUAS, then detection and interdiction capability is improved, but collateral damage to civilian infrastructure and GPS signals increases
Solution Approach 1:
The system applies different detection and countermeasure strategies to different types of targets based on their characteristics. Civilian drones receive different treatment than potential threats, with the ability to selectively apply countermeasures only to specific targets that match threat criteria, thereby avoiding collateral damage to benign objects.
Solution Approach 2:
The system modifies detection parameters and countermeasure intensity based on target classification. By changing parameters such as radar cross-section thresholds, RF signal characteristics, and countermeasure power levels according to the identified target type, the system achieves reliable threat detection while minimizing harmful effects on civilian infrastructure and GPS signals.
2Measurement precision
If military-grade detection systems are deployed, then target detection accuracy is improved, but system size, cost, and complexity increase making them unsuitable for civilian use
Solution Approach 1:
The detection system is divided into multiple independent sensor modules (radar, RF direction finding, electro-optical/infrared cameras, laser range finders) that can be selectively activated based on threat level and operational requirements. This modular segmentation allows the system to achieve military-grade detection accuracy when needed while maintaining a smaller, more cost-effective configuration for routine civilian surveillance.
Solution Approach 2:
The system integrates multiple sensor types that serve both civilian surveillance functions and military-grade threat detection. The same radar, camera, and RF systems used for general drone monitoring can also detect and classify potential threats with high precision, eliminating the need for separate specialized systems and reducing overall complexity and cost.
3Reliability
If broad-spectrum RF countermeasures are used to disrupt sUAS control systems, then interdiction effectiveness is improved, but interference with civilian GPS and communication signals increases
Solution Approach 1:
The system uses RF direction finding as an intermediary to precisely identify and locate the controlling transmitter of a target drone before applying countermeasures. By first determining the exact source and characteristics of the control signal, the system can then apply targeted countermeasures that disrupt only the specific drone's control link while leaving civilian GPS and communication signals unaffected.
Solution Approach 2:
The system replaces broad-spectrum RF jamming with precision-targeted countermeasures based on detailed spectral analysis of the target's control signals. By substituting mechanical/sweep-based jamming approaches with electronically-controlled, frequency-specific countermeasures guided by spectral identification, the system achieves effective interdiction without interfering with civilian GPS and communication frequencies.
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 effective detection and deterrence of sUAS with minimal collateral damage, offering increased security and privacy in civilian environments by using a combination of sensors and algorithms to precisely target and disrupt sUAS control systems without interfering with civilian navigation systems.
Implementation Method 1
radar data, RF data, and imagery data
Implementation Method 2
RF direction finding, electro-optical/infrared cameras
Implementation Method 3
electro-optical/infrared cameras
Implementation Method 4
laser range finders to detect, track, classify, and deter sUAS
Data Source
AI summary
A system for providing integrated detection and deterrence against an unmanned vehicle including but not limited to aerial and similar technology unmanned systems using a detection element, a tracking element, an identification element and an interdiction or deterrent element. Elements contain sensors that observe real time quantifiable data regarding the object of interest to create an assessment of the object of interests risk or threat to a protected area of interest. This assessment is based on the application of machine learning and data mining techniques and protocols to the set of data related to the object of interest. This data product is available to third parties via a set of defined interface protocols and available API. The deterrent element, using other machine learning or artificially intelligent algorithms, selects from a variable menu of possible deterrent actions not limited to those actions currently included in this embodiment, but includes those actions available from third parties such as specialized “hunter drones”. Though designed for autonomous action, a Human in the Loop may override the automated system solutions.