Drone Detection via Broad Spectrum Sound Matching
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Solution Overview
Problem
Current drone detection technologies, such as those using peak harmonic matching, are ineffective in identifying drones due to their varying shapes, sizes, and rotor configurations, which produce a wide range of tones, making it difficult to accurately detect and classify them.
Innovation Solution
The system employs broad spectrum matching, using a processor to convert sound samples into feature frequency spectra and compare them to a database of drone sound signatures, enabling precise detection and classification of drones regardless of their shape, size, or rotor configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If peak harmonic matching is used for drone detection, then the detection method is simple, but it cannot accurately detect drones with varying shapes, sizes, and rotor configurations
Solution Approach 1:
The patent changes the detection parameters from peak harmonic matching to broad spectrum matching across multiple frequency bands. This allows the system to capture the full frequency spectrum of drone sounds, enabling accurate detection and classification of drones with varying shapes, sizes, and rotor configurations while maintaining computational feasibility through structured frequency band analysis
2Measurement precision
If broad spectrum matching is used to detect all frequency tones, then drone detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the frequency spectrum into multiple discrete frequency bands, allowing the system to process broad spectrum data in manageable portions. Each frequency band is analyzed separately for drone sound signatures, then results are integrated to achieve accurate detection and classification without overwhelming computational complexity
Solution Approach 2:
The patent analyzes the entire frequency spectrum (excessive action) to ensure comprehensive drone detection, but uses efficient signal processing techniques to handle the data volume. By processing all frequency bands and comparing against drone sound signature databases, the system achieves superior accuracy while managing computational load through optimized algorithms
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
This approach allows for accurate and efficient detection and classification of drones, overcoming the limitations of peak harmonic matching by utilizing the entire frequency spectrum, enabling reliable identification of drones in diverse configurations.
Implementation Method 1
A drone detection device receives a sound signal in a microphone
Implementation Method 2
A processor within the device processes the recorded sound sample into a feature frequency spectrum
Data Source
AI summary
A system, method, and apparatus for drone detection and classification are disclosed. An example method includes receiving a sound signal in a microphone and recording, via a sound card, a digital sound sample of the sound signal, the digital sound sample having a predetermined duration. The method also includes processing, via a processor, the digital sound sample into a feature frequency spectrum. The method further includes applying, via the processor, broad spectrum matching to compare the feature frequency spectrum to at least one drone sound signature stored in a database, the at least one drone sound signature corresponding to a flight characteristic of a drone model. The method moreover includes, conditioned on matching the feature frequency spectrum to one of the drone sound signatures, transmitting, via the processor, an alert.