Effective recognition of radar jamming is of great importance in improving radar system's anti-jamming capability. Existing radar jamming recognition methods based on convolutional neural networks suffer from limitations such as low recognition accuracy and poor network stability in complex electromagnetic environments. This is due to incomplete extraction and utilization of useful information from the jamming signal. In this article, a few-shot adaptive confidence aggregation and cross-modal refinement jamming recognition (JR-ACAR) method is designed to mine the complete information of the jamming signal for enhancing both accuracy and robustness of the method. An adaptive confidence aggregation model is proposed to fully exploit the complementary information between the modulus, phase, and real and imaginary parts of the input time–frequency spectrum of jamming signal and adaptively aggregate this information by confidence vectors. Moreover, to further improve the recognition accuracy while enhancing the robustness of the network, a cross-modal refinement model is proposed to mine the correlation information between the aggregated jamming recognition result and the original input time–frequency image. Experimental results on both simulated and measured mixed dataset validate that the proposed JR-ACAR method is superior to the existing methods for radar jamming recognition in terms of recognition accuracy and robustness.
Few-Shot Radar Jamming Recognition Network via Complete Information Mining
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 3 ; 3625-3638
01.06.2024
7348737 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Radar Network Target Tracking in a Jamming Environment
British Library Conference Proceedings | 1996
|Radar network target tracking in a jamming environment
IEEE | 1996
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