ADAPTIVE METHODS TO COUNTER ACTIVE NOISE JAMMING
DOI:
https://doi.org/10.28925/2663-4023.2025.30.987Keywords:
anti-jamming capability; radio jamming; spoofing; jamming; electronic warfare; countermeasure methods; protection techniques; CRPA systems.Abstract
The effectiveness of performing a flight mission by an unmanned aerial vehicle at a given distance crucially depends on the quality of control, telemetry control and data transmission by the unmanned aerial vehicle, which, in turn, are determined by the quality of radio communication between the ground control station and the unmanned aerial vehicle. At the same time, modern military radio-electronic systems operate, as a rule, in a complex radio-electronic environment, caused by the influence of both internal and external interference, as well as enemy electronic suppression means. Active interference of various types and purposes is used, aimed at disabling control channels, navigation channels, telemetry channels and data transmission channels of unmanned aerial vehicles, in particular FPV drones, which necessitates the search and implementation of new countermeasure methods capable of ensuring the stable functioning of unmanned aerial vehicles and FPV drones in conditions of active information confrontation. The article considers modern approaches to counteracting active noise interference in FPV drone control systems. The relevance of the problem is substantiated, due to the increasing scale of the use of unmanned aerial vehicles in the military and civilian spheres, as well as increasing the effectiveness of electronic jamming. Traditional protection methods are analyzed and their limitations in dynamic conditions of information warfare are identified. The results of the study of adaptive LMS and RLS filters, spatial signal selection methods (MVDR, LCMV), as well as the use of machine learning algorithms for detecting and neutralizing jamming signals are presented. It is shown that the combination of adaptive and intelligent methods provides increased stability of communication channels and reduces the risk of loss of drone control. The conclusion is made about the feasibility of using hybrid solutions that integrate classical digital signal processing algorithms with artificial intelligence methods.
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