To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming c...To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.展开更多
Aim To get the theory base of designing FM fuze's jamming signal, its jamming mechanism was studied. Methods A sinusoidal FM fuze was analyzed in time domain and frequency domain and the concept of channel lea...Aim To get the theory base of designing FM fuze's jamming signal, its jamming mechanism was studied. Methods A sinusoidal FM fuze was analyzed in time domain and frequency domain and the concept of channel leak was presented. Results It was proved that information channel leak exists in FM fuze because of the nonlinear property of the mixer. The jamming signal was designed based on the channel leak and the jamming mechanism was analyzed in detail. Conclusion This kind of jamming signal can jam the sinusoidal FM fuzes effectively just depending on the jamming signal's feature itself. It's different from the traditional jamming way of simulating echo. Though the sinusoidal FM fuze was just analyzed, the principle is applicable to all FM fuzes. At the same time, it may be used as the reference for FM radar and communication countermeasures.展开更多
基金supported by the National Natural Science Foundation of China(U19B2016)Zhejiang Provincial Key Lab of Data Storage and Transmission Technology,Hangzhou Dianzi University。
文摘To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.
文摘Aim To get the theory base of designing FM fuze's jamming signal, its jamming mechanism was studied. Methods A sinusoidal FM fuze was analyzed in time domain and frequency domain and the concept of channel leak was presented. Results It was proved that information channel leak exists in FM fuze because of the nonlinear property of the mixer. The jamming signal was designed based on the channel leak and the jamming mechanism was analyzed in detail. Conclusion This kind of jamming signal can jam the sinusoidal FM fuzes effectively just depending on the jamming signal's feature itself. It's different from the traditional jamming way of simulating echo. Though the sinusoidal FM fuze was just analyzed, the principle is applicable to all FM fuzes. At the same time, it may be used as the reference for FM radar and communication countermeasures.
