Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
The processing and application of time series are widespread,including tasks like weather forecasting,traffic flow prediction and intention recognition.However,in reality,missing data often occurs due to target occlus...The processing and application of time series are widespread,including tasks like weather forecasting,traffic flow prediction and intention recognition.However,in reality,missing data often occurs due to target occlusion or sensor failures.Many deep learning models are designed for uniformly sampled complete data and cannot be directly applied to scenarios with missing values.Traditional data preprocessing methods,such as imputation and interpolation,introduce additional noise.To address these challenges,we propose an end-to-end model with Learnable Embedding and capture Multidimensional Features(LEMF).LEMF can directly handle real-world time series with missing values.We utilize the LE module to extract richer temporal information,compensating for the limitations of missing data.The MF module can extract features related to the relationships between variables.We leverage these hidden representations for intention recognition,which is the time series classification task.We thoroughly evaluate our model on a self-constructed intention dataset.Compared to baseline model,the LEMF model achieved an average of 10%higher accuracy at each missing ratio.Additionally,we validate the model’s generalization capabilities on two real-world datasets.Our model also shows optimal or suboptimal performance.展开更多
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
基金The research leading to these results received funding from National Natural Science Foundation of China under Grant Agreement No.61803260the Fundamental Research Funds for the Central Universities,Engineering Research Center of Aerospace Science and Technology,Ministry of Education.
文摘The processing and application of time series are widespread,including tasks like weather forecasting,traffic flow prediction and intention recognition.However,in reality,missing data often occurs due to target occlusion or sensor failures.Many deep learning models are designed for uniformly sampled complete data and cannot be directly applied to scenarios with missing values.Traditional data preprocessing methods,such as imputation and interpolation,introduce additional noise.To address these challenges,we propose an end-to-end model with Learnable Embedding and capture Multidimensional Features(LEMF).LEMF can directly handle real-world time series with missing values.We utilize the LE module to extract richer temporal information,compensating for the limitations of missing data.The MF module can extract features related to the relationships between variables.We leverage these hidden representations for intention recognition,which is the time series classification task.We thoroughly evaluate our model on a self-constructed intention dataset.Compared to baseline model,the LEMF model achieved an average of 10%higher accuracy at each missing ratio.Additionally,we validate the model’s generalization capabilities on two real-world datasets.Our model also shows optimal or suboptimal performance.
