Drone Detection Pods Using Multi-Modal Sensor Fusion
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Solution Overview
Problem
Current drone detection systems are expensive and not easily deployable, making it difficult for organizations to detect unauthorized drones flying over private property, which can capture sensitive information.
Innovation Solution
A system of strategically positioned pods equipped with video cameras, acoustic sensors, and RF antennas that use geo-fence technology and a Drone Object Recognition library to identify and differentiate between domestic and foreign drones, triggering alerts when a foreign drone is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expensive drone detection systems are deployed, then detection capability is improved, but cost and deployment difficulty increase
Solution Approach 1:
The detection system is divided into multiple independent sensor pods, each capable of autonomous detection. These pods can be strategically positioned at different locations to provide comprehensive coverage of a property or airspace, making the overall system more deployable and manageable while maintaining high detection reliability
Solution Approach 2:
Each sensor pod is designed as a multi-functional unit that integrates video cameras, acoustic sensors, and RF antennas to detect drones through multiple modalities simultaneously. This universal design allows a single pod type to perform comprehensive drone detection without requiring specialized equipment for each detection method
2Measurement precision
If comprehensive sensor arrays are used, then drone detection accuracy is improved, but system cost increases
Solution Approach 1:
A centralized server acts as an intermediary that receives data from multiple sensor pods, processes the information using machine learning algorithms, and coordinates the sensor arrays. This intermediary enables comprehensive multi-sensor detection while simplifying the overall system architecture by centralizing the complex processing functions
Solution Approach 2:
The system uses video cameras to capture visual copies/images of detected drones and acoustic sensors to capture audio copies of drone sounds. These digital copies are analyzed and stored, enabling accurate drone identification without requiring physical interception or capture of the drones themselves
3Reliability
If multiple detection modalities are integrated, then false positive reduction is improved, but processing complexity increases
Solution Approach 1:
The server implements feedback mechanisms where detection results from one sensor modality inform the operation of other sensors. When a drone is detected by one modality, the system adjusts other sensors to focus on the same area, and the combined results are continuously refined through machine learning algorithms that learn from each detection event to reduce false positives
Data Source
AI summary
One embodiment provides a method of capturing the presence of a drone, including: collecting, using at least one sensor, data associated with an aerial object; analyzing, using a processor, the data to determine at least one characteristic of the aerial object; accessing, in a database, a library of stored characteristics of commercially available drones; determining, based on the analyzing, if the at least one characteristic of the aerial object matches a characteristic of a commercially available drone; and responsive to the determining, generating an indication of a positive match. Other aspects are described and claimed.