ArticleSensors (Basel, Switzerland)2024
Classification of Unmanned Aerial Vehicles Based on Acoustic Signals Obtained in External Environmental Conditions.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A Lightweight Zero-Trust-Based Authentication Framework for UAV Systems.Sensors (Basel, Switzerland) · 2026Article
- Spatio-Temporal Feature Fusion for Anti-UAV Detection: Integrating Inter-Frame Dynamics and Appearance.Sensors (Basel, Switzerland) · 2026Article
- Experimental Study of Flame Extinguishing Using a Smart High-Power Acoustic Extinguisher: A Case of Distorted Waveforms.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
1 author.
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Abstract
Detection of unmanned aerial vehicles (UAVs) and their classification on the basis of acoustic signals recorded in the presence of UAVs is a very important source of information. Such information can be the basis of certain decisions. It can support the autonomy of drones and their decision-making system, enabling them to cooperate in a swarm. The aim of this study was to classify acoustic signals recorded in the presence of 17 drones while they hovered individually at a height of 8 m above the recording equipment. The signals were obtained for the drones one at a time in external environmental conditions. Mel-frequency cepstral coefficients (MFCCs) were evaluated from the recorded signals. A discriminant analysis was performed based on 12 MFCCs. The grouping factor was the drone model. The result of the classification is a score of 98.8%. This means that on the basis of acoustic signals recorded in the presence of a drone, it is possible not only to detect the object but also to classify its model.
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Registered trials
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