Mobile Emergency Perimeter System for UAS Detection
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Solution Overview
Problem
Current systems for detecting and mitigating unauthorized unmanned aerial systems (UAS) in wildfire areas are ineffective due to reliance on permanent infrastructure and the risk of interfering with emergency aircraft or communications, and existing detection systems only detect UAS presence without providing a solution for mitigation.
Innovation Solution
A mobile emergency perimeter system (MEPS) using a network of sensors with a wireless mesh network and a central processor to locate the source of RF signals from UAS controllers, employing multilateration and mesh networking to quickly identify and halt unauthorized UAS operations in dynamic and challenging environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If permanent infrastructure detection systems are deployed, then detection capability is improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The system divides the detection network into autonomous sensor nodes that can be independently deployed. Each node operates as a standalone unit with local processing capabilities, eliminating the need for complex centralized infrastructure while maintaining reliable detection coverage across the emergency perimeter.
Solution Approach 2:
The system transitions from static permanent infrastructure to dynamic mobile sensor nodes that can be rapidly deployed and repositioned. The nodes form an adaptive network that automatically configures itself based on the emergency perimeter requirements, reducing deployment complexity while improving detection reliability.
2Area of stationary object
If detection systems are deployed in rugged or obstructed terrain, then coverage area is improved, but system reliability deteriorates due to signal blockage
Solution Approach 1:
The system incorporates aerial drone nodes that operate in the three-dimensional airspace above rugged terrain. These aerial nodes provide line-of-sight RF signal paths that bypass ground-level obstructions, maintaining reliable communication and detection coverage across difficult terrain while expanding the effective coverage area.
Solution Approach 2:
The system uses mesh networking as an intermediary communication layer between sensor nodes. When direct signal paths are blocked by terrain, signals are routed through intermediate nodes, ensuring reliable transmission across obstructed terrain and maintaining network connectivity throughout the expanded coverage area.
3Measurement precision
If more sensors are deployed to improve location precision, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
Each sensor node autonomously performs signal processing, time synchronization, and location calculations using its own computational resources. This self-service capability eliminates the need for complex centralized processing infrastructure, allowing the system to achieve high location precision through distributed intelligence without proportionally increasing overall system complexity.
Solution Approach 2:
The system replaces physical sensor proliferation with computational multilateration algorithms. By using RF signal time-of-arrival measurements and mathematical position calculation, the system achieves precise location determination with fewer physical sensors, reducing hardware complexity while maintaining high measurement precision.
4Loss of time
If rapid deployment is implemented to respond to dynamic wildfire situations, then response time is improved, but manufacturing precision and system configuration accuracy worsen
Solution Approach 1:
Sensor nodes are pre-configured with emergency response protocols, identification algorithms, and operational parameters before deployment. This preliminary configuration allows nodes to be rapidly deployed without requiring complex on-site setup, achieving both rapid response time and accurate system configuration through pre-programmed intelligence.
Solution Approach 2:
The system uses software-defined parameters that can be dynamically adjusted after deployment. Nodes automatically adapt their operational characteristics based on the emergency situation, allowing rapid deployment with flexible configuration that maintains accuracy through software-based parameter optimization rather than rigid preconfiguration.
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 MEPS effectively detects and locates unauthorized UAS controllers, providing alerts and determining their approximate location, allowing authorities to manage and mitigate unauthorized UAS operations in real-time, even in rugged or obstructed terrain, thereby enhancing wildfire response and safety.
Implementation Method 1
receive time of arrival data for a RF signal transmitted by an emitter and received at each of the plurality of sensors
Implementation Method 2
determine a three-dimensional (3-D) estimate of the geographical location of the emitter
Data Source
AI summary
A method for minimizing aircraft collisions, includes detecting a flight of an unmanned aerial system (UAS) in a restricted area and determining a location of a radio frequency (RF) emitter in communication with the UAS. The method includes, at each of a plurality of RF sensors of a network of wireless RF sensors, receiving RF emissions within an RF band pertaining to UAS control, processing the received RF emissions, and transmitting data derived from the processed RF emissions. The method further includes at a designated one of the plurality of RF sensors, receiving the transmitted data from the RF sensors, a processor computing, using the transmitted data received from the RF sensors, a location estimate for the RF emitter and to predict the UAS is flying, and based on the prediction, the processor generating an alert.