Specific Emitter Identification Method Based on I/Q Imbalance with SNR Estimation U sing Wavelet Denoising

A new method of specific emitter extraction based on I/Q imbalance is proposed in this paper. Firstly, the modulated noisy signal is processed and the fingerprint features related to the I/Q mismatch parameters are extracted. This feature is related to two parts. One part can be obtained directly by signal processing of the modulated noisy signal, and the other part requires the SNR estimation of the modulated signal. The signal to noise ratio(SNR) estimation algorithm based on wavelet denoising is adopted in this paper. Starting from the continuity of the threshold function and its derivative function, the method improves the traditional wavelet threshold function, constructs the threshold function with adjustable parameters, and uses the signal before and after the denoising to estimate the SNR. Experimental results show that the SNR and mean square error (MSE) of the new threshold function denoising algorithm are improved by 4 % and 16 % respectively compared with traditional algorithm. The SNR estimated by wavelet denoising is used as the fingerprint feature of specific emitter identification, and the specific emitter identification effect under different SNR is analyzed. Compared with bisspectral and Hilbert-Huang transform identification algorithms, the identification rate of this method is better. Compared with SNR estimation algorithm based on eigenvalue decomposition, the identification accuracy is improved by 3%.