Multi-Sensor UAV Tracking With RF-Based Countermeasure Targeting
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Solution Overview
Problem
The anonymous nature of unmanned aerial vehicles (UAVs) poses challenges in ensuring airspace safety and accountability in critical locations, as they can compromise security and pose risks to areas like airports, prisons, and residential homes, necessitating a system to detect, identify, track, and manage UAVs.
Innovation Solution
A system utilizing video, audio, Wi-Fi, and radio frequency sensors to collect and process data for UAV detection, identification, and tracking, with portable countermeasure devices to disrupt UAVs when necessary, supported by a central system that processes sensor data and manages a catalog of recognized UAVs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and processing systems are deployed to detect and identify UAVs, then measurement precision and reliability of UAV detection is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (video, audio, Wi-Fi, RF sensors) into an integrated sensor system that operates together to detect and identify UAVs. This merging of sensors allows the system to overcome the limitations of individual sensors by cross-validating detections and reducing false positives, thereby improving measurement precision while managing complexity through unified system architecture.
Solution Approach 2:
The sensor system is designed with multi-functionality, where each sensor type serves multiple purposes: video sensors detect visual presence and track movement, audio sensors identify drone motor sounds, Wi-Fi sensors detect communication signals, and RF sensors monitor radio frequencies. This universal approach allows a single integrated system to perform detection, identification, and tracking functions across different modalities, improving reliability without proportionally increasing complexity.
2Reliability
If portable countermeasure devices are deployed to disrupt unauthorized UAVs, then airspace security is improved, but device complexity and operational complexity increase
Solution Approach 1:
The system performs preliminary detection, identification, and classification of UAVs before deploying countermeasures. The sensor system continuously monitors the airspace and pre-identifies potential threats by analyzing sensor data patterns. This preliminary action allows the system to be ready to respond quickly when unauthorized UAVs are detected, improving security response time while automating the decision-making process to reduce operational complexity.
Solution Approach 2:
The central processing system acts as an intermediary between sensor detection and countermeasure deployment. It receives data from multiple sensors, processes the information to determine if a UAV is unauthorized, and then triggers the appropriate countermeasure device. This intermediary layer simplifies operation by automating the complex decision-making process, allowing operators to simply monitor the system rather than manually analyze sensor data and make deployment decisions.
3Reliability
If a comprehensive sensor system is used to distinguish between malicious and benign UAVs, then measurement precision and reliability are improved, but loss of time for data processing increases
Solution Approach 1:
The system implements a tiered detection approach where it first performs partial analysis using the most reliable and quickest sensor types (such as audio and RF sensors for immediate threat detection). Only when initial detection triggers further investigation does the system engage in more comprehensive analysis using all sensor types. This partial action approach maintains high reliability for critical detections while reducing average processing time by not always deploying the full analytical capacity.
Solution Approach 2:
The sensor system operates continuously, with sensors constantly monitoring the airspace and data processing occurring in real-time streams rather than batch processing. This continuity allows the system to maintain reliable identification of UAVs as they enter the monitoring zone without requiring retrospective analysis, reducing the effective processing time while maintaining high reliability through continuous validation across multiple sensor inputs.
Data Source
AI summary
Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. A tracking system can receive sensor data associated with an object in a particular airspace from one or more radio frequency sensors. The tracking system can analyze the sensor data relating to the object to identify a type of RF signal being used by the object. A portable countermeasure device can generate one or more disruption signals on one or more targeted bands of spectrum based on the type of RF signal being used by the object.