DETECTION AND CLASSIFICATION OF AIRBORNE OBJECTS BASED ON MACHINE LEARNING: CURRENT STATE OF RESEARCH

Abstract

The article provides a comprehensive overview of modern technologies for detecting airborne objects. It emphasizes the use of machine learning (ML) methods to improve the accuracy and reliability of surveillance systems. The study is relevant due to the growing number of airborne objects that threaten civil and military infrastructure. Traditional detection methods also have limitations in adverse weather conditions or when radio interference is present. Intelligent technologies, capable of recognizing object behavior patterns and functioning under uncertainty, are especially important in this context.

The primary approaches to detecting aerial objects are analyzed, including radio frequency monitoring, radar analysis, electro-optical and thermal imaging surveillance, and acoustic monitoring. Their advantages, limitations, and specific applications are identified. Special attention is paid to systems that use ML algorithms for automated object recognition and classification. Thanks to its ability to independently detect patterns in data without human intervention, machine learning ensures high accuracy and reduces the influence of the human factor.

The paper summarizes the results of recent studies that demonstrate the accuracy of detection in test environments, the ability of systems to adapt to new scenarios, and the improvement in reliability through multisensor integration. It is shown that combining data from different sensors – radar, EO/IR, acoustic, and radio frequency – in conjunction with ML algorithms creates the basis for building adaptive airspace monitoring systems.

The conclusions emphasize that the effectiveness of combating air threats depends on the ability of systems to integrate heterogeneous data and use intelligent analysis algorithms, which paves the way for the creation of accurate, flexible, and reliable means of detecting new types of airborne objects.