A multi-order Fractional Fourier domain feature union method for active sonar target classification

Abstract

Different order domains in Fractional Fourier Transform (FrFT) reflect the features of different time–frequency planes. They contain multi-domain information about the target. The optimal order domain feature is used widely because of its anti-reverberation interference performance, but other order domains also have anti-reverberation ability, and even have more separable performance. It is of great significance to use effectively the multi-order domain feature for target classification and recognition. Therefore, a multi-order Fractional Fourier domain feature union method based on the information entropy is proposed in this paper. This method aims to find more distinguishable and separable target features than that of the optimal order domain by calculating the information entropies of the amplitude features and union them by determining the weights according to the value of the information entropies. The performance of the presented method is verified using the field data of four types of similar targets’ echo signals when the signal-to-reverberation ratios (SRRs) are respectively 4 dB, 0 dB, −3 dB, and −6 dB. Experimental results show that the method proposed in this paper can reflect the overall characteristics of the signals and has a more distinguishable ability and a good anti-reverberation ability.