Crowdsourced Drone Tracking Using Distributed Mobile Sensors
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Solution Overview
Problem
There is a need for an effective crowd-sourced unmanned aerial vehicle detection and tracking system using distributed mobile device sensors to address public safety, national security, and airspace management issues related to unauthorized drone activity.
Innovation Solution
A system utilizing a plurality of mobile devices with sensors, processors, and systemic software to detect, track, and transmit aerial vehicle data, including GPS coordinates, altitude, speed, and trajectory, with data fusion and notification engines to generate alerts and integrate with military situational awareness systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed mobile device sensors are used for aerial vehicle detection, then detection coverage and response speed are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system divides the detection task into multiple independent mobile device sensors distributed across different locations. Each device independently monitors for aerial vehicles using its own sensors (camera, GPS, accelerometer), and results are aggregated centrally. This segmentation enables wide geographic coverage while keeping individual device complexity low.
Solution Approach 2:
Data from multiple distributed mobile devices is merged and aggregated at a central server or cloud platform. The system combines sensor readings, GPS coordinates, and detection events from numerous devices to create a comprehensive aerial vehicle tracking system, achieving high detection coverage through data consolidation.
2Measurement precision
If multiple sensors and data fusion are implemented, then tracking precision and reliability are improved, but computational load and processing time increase
Solution Approach 1:
Mobile devices continuously collect and pre-process sensor data (camera feeds, GPS location, accelerometer readings) in the background before aerial vehicle detection is needed. This preliminary data gathering and filtering reduces the computational burden during actual detection events, maintaining high tracking precision while minimizing processing delays.
Solution Approach 2:
A central server or cloud platform acts as an intermediary between distributed mobile devices and the final tracking output. This intermediary performs data fusion, correlating sensor data from multiple devices, filtering false positives, and generating consolidated tracking information, thereby distributing computational load and reducing processing time at individual device levels.
Data Source
AI summary
A crowdsourcing method to track and report of aerial vehicles is disclosed. Fusing sensed data from a plurality of mobile devices, made interoperable via systemic software installed on each mobile device, the method collectively and collaboratively provides warning, notification and characterization of one or more unmanned aerial vehicles to law enforcement and/or other responder agencies.