Specific Emitter Identification Based on Homomorphic Filtering and Support Vector Machine-2K

Specific emitter identification (SEI) is the process of identifying or discriminating different emitters by extracting the radio frequency fingerprints from the received signals. A novel SEI scheme with two steps is proposed in this paper. In the first step, the new fingerprint features are extracte...

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Bibliographic Details
Published in2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) pp. 1 - 4
Main Authors Wu, Qi, Hu, Zepeng, Wan, Qun
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.10.2022
Subjects
Benchmark testing
Feature extraction
Filtering
Fingerprint recognition
Homomorphic filtering
Signal processing
Simulation
Specific emitter identification
Support vector machine classification
Support Vector Machine-2K
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Summary:Specific emitter identification (SEI) is the process of identifying or discriminating different emitters by extracting the radio frequency fingerprints from the received signals. A novel SEI scheme with two steps is proposed in this paper. In the first step, the new fingerprint features are extracted as the emitter-irrelated information is suppressed by homomorphic filtering. Then, Two View SVM-2K (Support Vector Machine on two Kernels) classifier is exploited to classify emitters effectively based on the above features. Simulation results show that the proposed method achieved better classification performance than the benchmark method.
DOI:10.1109/ICSPCC55723.2022.9984423