AI Article Synopsis
- The study focuses on classifying unmanned aerial vehicles (UAVs) based on acoustic signals they produce while hovering at 8 meters, which is crucial for decision-making in drone operations.
- It involved recording sounds from 17 different drone models individually and analyzing these sounds using Mel-frequency cepstral coefficients (MFCCs).
- The classification achieved a high accuracy score of 98.8%, demonstrating the potential to both detect UAVs and identify their specific models through their acoustic signatures.
Article 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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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11397886 | PMC |
| http://dx.doi.org/10.3390/s24175663 | DOI Listing |
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