基于递进神经网络的混叠干扰识别技术

doi:10.12305/j.issn.1001-506X.2025.10.12

Abstract

Abstract:

In practical electromagnetic warfare scenarios, where multiple jammers operating in coordination create a complex environment with various radar jamming signals existing simultaneously, traditional convolutional neural networks （CNNs） struggle with the scale and specificity required for effective interference classification. This paper introduces a stepwise CNN, which addresses these challenges by employing a staged approach to separately extract overlap type feature and interference type features. Through time-frequency analysis of various overlapping jamming signals, training and fest datasets are developed to train the network. Simulation experiments show that the proposed network achieves a recognition accuracy rate of over 99.3889% for fifteen different jamming signals under three types of mixed interference conditions. The recognition performance under different signal-to-noise ratio conditions is significantly better than traditional CNNs.

Key words: stepwise neural network, overlap interference, feature extraction, interference recognition

CLC Number:

TN 972

Yifan FU, Hang RUAN, Heqiang MU, Dongping ZHOU, Li PAN, Lei LEI, Jiarui BAO. Overlapping interference recognition based on stepwise neural network[J]. Systems Engineering and Electronics, 2025, 47(10): 3251-3256.

Figures/Tables 8

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Overlapping interference recognition based on stepwise neural network

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