# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 1 Introduction  
The use of underwater acoustic signals for the purpose of identifying underwater targets has immense importance in the domains of marine resource exploitation and national defense security. The techniques for identifying underwater targets can be separated into two stages: extracting signal characteristics and developing an automated target classifier. The feature extraction method of early radiation noise is analyzed around the target energy spectrum and line spectrum characteristics. The continuous spectrum of the radiated noise may be accurately modeled using either the least square approach or power spectrum estimation. By eliminating the component of the continuous spectrum from the original signal, we can extract the line spectrum properties of the signal [1]. With the progress of neural networks, the energy spectrum features of underwater acoustic signals can be extracted using neural networks. As a relatively mature neural network, the Restricted Boltzmann machine can achieve high-precision extraction of ship target power spectrum features [2]. When examining underwater acoustic targets using signal time domain analysis, the cross-power spectrum obtained from coherence in the signal time domain may emphasize the distinctive line spectrum features of underwater targets [3]. The primary emphasis in the study of early underwater acoustic targets is on the temporal characteristics of the signal. However, the underwater acoustic signal is unstable and cannot accurately describe the signal characteristics in the time domain; so, the frequency domain of the signal can reflect the signal characteristics more stably. The double logarithmic spectrum feature is used to analyze the frequency domain features of ship radiation noise [4], and itimproves the accuracy of target recognition. A single time domain or frequency domain feature contains less target feature information, and the Time-Frequency combined analysis can more comprehensively analyze the components of underwater acoustic signals that do not change with time. [PERSON] et al. [5] extracted multi-beam low-frequency analysis and recording features, used convolutional neural networks to identify targets, and realized target detection. With the improvement of wavelet analysis technology, its excellent non-stationary signal analysis ability has received attention in the underwater acoustic field; so, it has been widely used in the analysis of radiation noise. Wavelet analysis is used to decompose underwater acoustic signals, and the wavelet coefficients extracted from the respective signals can reflect the characteristics of the target [6], which can not only improve the accuracy of the target recognition but also provide a new idea for signal denoising. The combination of empirical mode decomposition (EMD) and wavelet decomposition is used to remove noise from underwater acoustic targets, resulting in signals with a high ratio of signal to noise. This process enhances the accuracy of target detection [7]. With the development of the chaos theory and nonlinear dynamic principle, it is frequently applied to the characteristic analysis of underwater acoustic targets. Refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) is proposed to extract ship features, and the experimental results show the validity and universality of the classification [8]. Combined with improved intrinsic time-scale decomposition (IITD) and multiscale dispersion entropy (MDE), it can significantly raise target recognition accuracy [9]. The majority of the aforementioned feature extraction techniques concentrate on the signal's Time-Frequency domain features. The overlapping feature frequency ranges of various underwater acoustic targets provide an immediate challenge in accurately identifying the target when using the signal's characteristics in the Time-Frequency domain as a result of the fast progress in oceanic technology.