Recognition method for underwater imitation whistle communication signals by slope distribution

•In view of the bionic covert UAC signals based on the TF contour coding, we propose a recognition method for BBOKMSs by the slope distribution, and the recognition accuracy can reach 90% at a SNR of −5 dB in the constructed underwater aquatic channels.•Aiming at TF contour extraction of whistles and BBOKMSs in the complex time-varying noise environment, a dynamic adaptive threshold segmentation method is proposed. The segmentation threshold can be dynamically adjusted according to the TF distribution of noise, having strong environmental adaptability.•In order to extract the classification characteristic parameters of real whistles and BBOKMSs, the PDE curve of slopes is calculated by the Gaussian kernel PDE, in addition, the peak points of the PDE curve are found and used as the classification basis of SVM. 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.