Communication Interference Recognition Based on Improved Deep Residual Shrinkage Network

In complex battlefield environments, Flying Ad-hoc NETwork (FANET) faces challenges of manually extracting communication interference signals features, low recognition rate in strong noise environment, and inability to recognize unknown interference types. To solve these problems, one Simple Non-local Correction Shrinkage (SNCS) module is constructed, which modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local Importance-based Pooling (LIP) is introduced to enhance the useful features of interference signals to reduce noise in the downsampling process, and the joint loss function is constructed by combining cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed, and the One Class Support Vector Machine Simplified Non-local Residual Shrinkage Network (OCSVM-SNRSN) model with the ability of both known class recognition and new class rejection is constructed by combining OCSVM and SNRSN. Experimental results show that the recognition accuracy of the OCSVM-SNRSN model is the highest in the scenario of low Jamming Noise Ratio (JNR). The accuracy is increased by about 4%-9% compared with other methods on the known class interference signal dataset, and the recognition accuracy reaches 99% when the JNR is -6dB. At the same time, compared with other methods, the False Positive Rate (FPR) for recognizing unknown class interference signals drops to 9%.