Recognition method for underwater imitation whistle communication signals by slope distribution

Abstract

Underwater bionic covert communication has been extensively studied. However, there are little researches on bionic communication signals recognition. We propose a recognition method for bionic binary orthogonal keying modulated signals (BBOKMSs) coded by time frequency (TF) contour. The BBOKMSs realize the covert communication by mimicking the real dolphin whistles with a series of segmented chirp rate modulated signals. We first put forward a TF contours extraction method by the dynamic adaptive threshold calculated by the cross sampling. Next, the slope of TF curve is modeled based on Gaussian kernel probability density estimation (PDE) for the slope distribution. Based on the characteristics of BBOKMSs, the characteristic parameters of PDE curve are calculated and used to recognize the BBOKMSs by support vector machine (SVM). Finally, the numerical simulations are carried out to measure the effectiveness of the recognition method proposed under different SNRs, coding parameters, and channels in this paper. It is shown that when the modulation slope is higher than 20 kHz/s and the code length is longer than 20 ms, the recognition accuracy can reach more than 90% at a SNR of −5 dB in the constructed underwater aquatic channel.