## 1 Introduction  
Interference mitigation and the enhancement of environmental adaptability are the primary research focuses in the active sonar target recognition field. Interference factors mainly include various forms of responsive and co-frequency interferences. Environmental factors primarily involve variations in the target's relative attitude, fluctuations in the underwater acoustic channel, and interface reverberation, among others. Specifically, the relative attitude of the target can be formally defined as the angular difference between the observation direction and the orientation of the target's tail. This parameter plays a critical role in analyzing the scattering characteristics of targets within sonar systems and can be further categorized into the relative horizontal azimuth angle (RHOA) and relative pitch angle (RPA). This study primarily investigates the adaptability of the RHOA in active sonar systems. Traditional target recognition methods based on parameter estimation [1, 2, 3] exhibit limitations such as a relatively short effective range, insufficient environmental adaptability, and restricted capability to resolve interferences. In recent years, the integration of feature extraction with deep learning techniques for underwater target recognition has emerged as a significant research focus. The recognition method, which focuses on extracting the geometric structural features of targets, has garnered significant attention in the field. As reported by the authors of [4; 5], algorithms of high-resolution DOA estimation have been utilized to estimate the geometric structures of underwater targets, with experimental validation carried out in an anechoic water tank. Chirplet atom decomposition was adopted to improve the extraction of geometric features from underwater targets [6]. In [7; 8], forward-looking imaging sonar was employed to acquire images and achieve target recognition. These studies primarily concentrated on static underwater targets. In contrast, a novel approach was proposed for extracting the highlight images of moving underwater targets, with the effectiveness of the algorithm being validated through experimental data [9]. The extraction of time-frequency features is one of the key research areas in underwater target recognition, as it reflects the scattering distribution characteristics of the target and requires a relatively low signal-to-noise ratio. As reported by the authors of [10; 11], the WVD time-frequency features of underwater target echoes were extracted, and the algorithm was validated using scaled pool model data. As conducted by the authors of [12; 13], the STFT time-frequency spectrum and wavelet scale spectrum were extracted, and a CNN network was employed to classify spherical targets of varying volumes. Furthermore, the authors of [14] extracted the STFT and WVD time-frequency spectra of underwater targets, and multi-domain feature fusion was implemented via a network to distinguish between targets and clutter. When radar and sonar detect moving targets, the echoes exhibit Doppler effects. Additionally, the nutation, precession, and partial rotation of the target produce micro-Doppler phenomena [15]. In radar applications, micro-Doppler features have been extensively utilized for identifying airborne and land-based targets [16; 17]. In contrast, in sonar systems, Doppler phenomenon is typically employed for target velocity estimation, detection, and tracking [18; 19]. As reported by the authors of [20], the Doppler characteristics of underwater rotating objects were investigated using a 1 MHz carrier signal. Water tank experiments confirmed the presence of micro-Doppler phenomena in underwater targets. However, due to the weak echo intensity of the propeller and the challenges posed by propagation attenuation and environmental noise, extracting micro-Doppler signatures remains difficult. The key challenges that remain to be addressed in the aforementioned research on active sonar target recognition are summarized in Table 1.