Specific Emitter Identification based on nonlinear complexity of signal

Specific Emitter Identification (SEI) is to identify the emitters with various RF fingerprints, originated from the nonlinearity of the emitter power amplifiers. This paper firstly develops an improved Approximate Entropy (imApEn) algorithm, by modifying the tolerance interval of Approximate Entropy (ApEn), to extract the nonlinear complexity of the signals as a new steady-state RF fingerprint. Then a novel identification algorithm is proposed based on the combination of the EMD and the imApEn, which utilizes some independent RF fingerprints to form multi-dimensional feature space and then the emitter classification is performed by the support vector machine. Additionally, the noise immunity and robustness of the imApEn are evaluated via Logistic map with different parameters. Computer simulations are conducted under additive white Gaussian noise as well as impulsive noise and the results demonstrate that the proposed algorithm significantly out-performs the energy-entropy algorithm and correlation algorithm based on the Hilbert-Huang Transform.