At the Yuri Gagarin State Technical University of Saratov (SSTU named after Yuri Gagarin), a system was created for recognizing types of unmanned aerial vehicles (UAVs) by their acoustic signals.
The system, developed by representatives of the university, is able to overcome factors that limit the recognition of unmanned aerial vehicles, effectively work in urban noise and poor visibility conditions, and adapt to new drone models.
During the development of the software, a comprehensive study was conducted, which made it possible to identify the key limitations of existing drone detection systems. As a result, a hybrid approach was proposed, combining methods of frequency-time analysis of sound with deep learning algorithms based on artificial intelligence.
The author of the project, Sergey Kuznetsov, noted that the expansion of the UAV functionality is associated with an increase in the risks of their unauthorized use.
Traditional identification methods - visual or radar detection - have limitations: they are ineffective in poor visibility conditions, require significant resources and do not always ensure stealth. Therefore, the development of alternative approaches, including the analysis of acoustic signals generated by UAVs, is becoming an urgent task.
The developed system is integrated into the existing infrastructure and operates on standard equipment. This reduces the risks of unauthorized use of UAVs through early detection. Testing has confirmed the effectiveness of the system in an urban environment, where it correctly identifies the acoustic patterns of drones against the background of noise.
Earlier www1.ru reported that the production of the Leader unmanned aerial system has been established in St. Petersburg.
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