Multi-Sensor UAV Identification for Airspace Monitoring
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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 the security of areas like airports, prisons, and residential homes, necessitating a system to detect, identify, and manage UAVs effectively.
Innovation Solution
A system utilizing video, audio, Wi-Fi, and radio frequency sensors to collect and process data to identify, track, and manage UAVs, employing confidence measures and pattern recognition to confirm UAV presence, and store information in a database for management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors (video, audio, Wi-Fi, RF) are deployed to detect and identify UAVs, then the detection capability and identification accuracy improve, but the system complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (video, audio, Wi-Fi, RF) into an integrated sensor network system that collectively detects and identifies UAVs. Each sensor type targets different aspects of UAV presence, and their data is fused to achieve comprehensive identification accuracy while managing system complexity through unified architecture.
Solution Approach 2:
The sensor network is designed with multi-functional capabilities where each sensor can serve multiple purposes. For example, RF sensors detect both control signals and communication signals from UAVs, while video sensors provide both visual detection and identification functions, reducing the need for dedicated single-purpose sensors.
2Speed
If sensor data is processed locally at each sensor node, then response time improves, but the computational requirements and energy consumption increase
Solution Approach 1:
The system architecture segments processing tasks between edge devices and central servers. Simple detection and filtering operations are performed locally at sensor nodes for immediate response, while complex data fusion and analysis are offloaded to centralized servers, balancing response time requirements with energy consumption constraints.
Solution Approach 2:
Local processing at sensor nodes performs only partial processing (basic detection and filtering) rather than complete data analysis. This allows the system to achieve timely response for critical detections while minimizing energy consumption by leaving more computationally intensive tasks for centralized processing.
Data Source
AI summary
Systems, methods, and apparatus for identifying and tracking UAVs including an image capturing device. A computing device can receive a frame captured via an image capturing device configured to monitor a particular air space. The computing device can identify a region of interest (ROI) in the frame. The ROI can include an image of an object. The computing device can perform a background subtraction process on the frame. The computing device can scale the frame to a uniform size. The computing device can perform a comparison of the frame to reference images. The reference images can include known unmanned aerial vehicle (UAV) images and known non-UAV images. The computing device can classify the object with a UAV classification based on the comparison.