Mobile Emergency Perimeter System for RF UAS Detection
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Solution Overview
Problem
Current systems fail to effectively detect and mitigate unauthorized unmanned aerial systems (UAS) in restricted wildfire areas, posing risks to firefighting operations and aircraft safety, as existing detection systems require permanent infrastructure and are ineffective in dynamic wildfire environments.
Innovation Solution
A mobile emergency perimeter system (MEPS) utilizing a network of sensors with a wireless mesh network and a central processor to locate and track radio-frequency signals from UAS controllers, employing multilateration and mesh networking to establish a dynamic perimeter around wildfires, allowing for rapid deployment and reconfiguration in various terrains and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If permanent infrastructure detection systems are deployed, then detection reliability is improved, but deployment time and adaptability to dynamic wildfire environments deteriorate
Solution Approach 1:
The system transitions from static permanent infrastructure to a dynamic mobile sensor network that can be rapidly deployed and repositioned. Sensors are mounted on mobile platforms (vehicles, aircraft, drones) allowing the detection perimeter to move with the wildfire and adapt to changing conditions while maintaining reliable RF signal detection capabilities.
Solution Approach 2:
The detection system is divided into multiple independent mobile sensor nodes that can operate autonomously or cooperatively. Each sensor unit is a self-contained module with RF detection, GPS定位, and communication capabilities, allowing flexible deployment configurations and easy repositioning without requiring permanent infrastructure.
2Adaptability or versatility
If mobile sensors are used, then adaptability to dynamic environments is improved, but measurement precision of RF signal location deteriorates
Solution Approach 1:
The system continuously receives feedback from GPS receivers on each mobile sensor node about their positions, and the central processor uses this information to dynamically update the multilateration calculations. This real-time feedback loop compensates for sensor movement and maintains accurate location determination of RF emitters despite the mobile platform.
Solution Approach 2:
Each mobile sensor node is pre-equipped with integrated GPS receivers and timing synchronization capabilities before deployment. This preliminary setup ensures that when sensors are moved to new positions, they immediately begin collecting precise location and timing data needed for accurate multilateration without requiring recalibration or setup time.
3Loss of time
If geo-fencing software updates are required, then loss of time in enforcement is improved, but reliability of emergency response deteriorates
Solution Approach 1:
The mobile sensor network performs self-service by autonomously detecting RF signals, determining emitter locations through multilateration, and generating alerts without requiring external software updates or manual configuration. The system automatically adapts to emergency situations by detecting unauthorized UAS operations and providing real-time location data to authorities.
Solution Approach 2:
The system replaces the software-based geo-fencing approach with a hardware-based RF signal detection and multilateration system. Instead of relying on UAS operators to update control software with restricted area coordinates, the mobile sensors passively detect RF emissions and independently determine locations, eliminating the software update delay and providing immediate enforcement capability.
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 identifies, locates, and tracks unauthorized UAS controllers, providing real-time alerts and location data to authorities, thereby enhancing the safety of firefighting operations and preventing UAS-related disruptions in wildfire suppression efforts.
Implementation Method 1
A mobile emergency perimeter system (MEPS) utilizing a network of sensors with a wireless mesh network and a central processor to locate and track radio-frequency signals from UAS controllers
Implementation Method 2
employing multilateration and mesh networking to establish a dynamic perimeter around wildfires
Data Source
AI summary
A computer-implemented method for establishing and controlling a mobile perimeter and for determining a geographic location of an RF emitting source at or within the mobile perimeter includes receiving from RF sensors in a network, processed RF emissions from the source collected at RF sensors. The RF emissions follow a wireless protocol and include frames encoding RF emitting source identification information. The method further includes extracting RF emitting source identification information from the frames, processing the source identification information to identify the RF emitting source, and classifying the RF emitting source by one or more of UAS type, UAS capabilities, and UAS model. The method also includes receiving from the RF sensors, a geographic location of each RF sensor and a time of arrival (TOA) of the RF emissions at the RF sensor; and executing a multilateration process to estimate a geographic location of the RF emitting source.