DEVELOPMENT OF A MODEL FOR DETECTION OF LOW-OBSERVATION TARGETS
DOI:
https://doi.org/10.18372/2310-5461.67.20349Keywords:
radio frequency monitoring, low-visibility targets, spectral analysis, digital processing, adaptabilityAbstract
The article presents the results of a comprehensive study of methods for detecting low-observation unmanned aerial vehicles (UAVs) using SDR (Software Defined Radio) technologies. Given the growing threats associated with the use of small-sized and low-power UAVs for reconnaissance and sabotage purposes, special attention is focused on methods of passive radio frequency monitoring and detection of weak signals in a complex spectral environment. The paper presents a detailed analysis of modern approaches to UAV detection using classical RF monitoring systems and their limitations, in particular, sensitivity to short-term and low-power signals, as well as processing delays and low adaptability.
The use of SDR is proposed as a flexible platform for building adaptive UAV detection systems that combine spectral analysis, machine learning and digital signal processing algorithms. The article contains a mathematical detection model that takes into account the factors of signal energy distribution, noise, spectrum width and multipath. The results of practical experiments with SDR detector models in various environmental conditions are presented, which confirm the effectiveness of the proposed approach in terms of reliability, detection delay and resource consumption.
The obtained conclusions demonstrate the feasibility of further implementation of SDR-oriented methods in critical infrastructure protection systems, as well as the creation of mobile complexes for autonomous monitoring of airspace in conditions of limited computing resources. The results of the work have applied significance for the defense forces, civil security and technical support of facilities with increased requirements for early detection of air threats.
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