## 1 Introduction  
Owing to their low-frequency characteristics, slow attenuation rate, and rich spectral composition, ship shaft-rate electric field signals have emerged as a critical modality in underwater target detection following acoustic field-based approaches [1]. The spectral components primarily comprise static electric field frequencies and shaft-rate electric field frequencies. Static electric field components correlate with hull corrosion levels, while shaft-rate electric field components reflect the rotational dynamics of propeller systems. These distinct frequency signatures provide clear discriminative indicators for target presence detection in submerged environments. However, how to effectively extract target features in complex marine environments is a vital and hot issue in current research on underwater target detection. Traditional signal feature extraction methods mainly include time-domain signal structure, Fourier transform of time-domain signals, classical spectrum estimation, and wavelet transform. Vessel shaft frequency electric field signals have non-stationaryand nonlinear characteristics, making it difficult to obtain effective parameters directly reflecting target features from the raw signals. Fourier transform integrates over the entire time domain of the signal, making it unable to reflect the time-varying characteristics of non-stationary electric signals clearly. Wavelet transform, influenced by wavelet basis functions, is prone to energy leakage issues in signals, thus failing to extract time-domain signal features accurately. In summary, traditional signal feature extraction methods can no longer solve the feature extraction problem for complex shaft frequency signals under low signal-to-noise ratio conditions [2; 3]. In recent years, with the rapid development of signal decomposition algorithms and nonlinear dynamic analysis methods, the updating and improvement of signal processing techniques, especially signal feature extraction techniques have been accelerating [4; 5; 6]. On the one hand, nonlinear dynamic analysis methods can provide the nonlinear characteristics required by signal processing techniques, many of which have been proven more effective than traditional energy and frequency characteristics. On the other hand, signal decomposition algorithms can break down complex non-stationary signals into a series of sub-signals based on different criteria. This enriches the quantity of signal features and highlights the differences between sub-signals. Empirical Mode Decomposition (EMD), a classic signal decomposition algorithm based on the Fourier transform, decomposes signals based on the time-scale characteristics of the signals without the need for presetting basis functions. When performing multi-scale decomposition of non-stationery and time-varying signals, it exhibits adaptability compared to traditional wavelet transform methods [7]. Based on the EMD method, scholars have successively proposed Ensemble Empirical Mode Decomposition (EEMD) and Intrinsic Computing Expressive Empirical Mode Decomposition with Adaptive Noise (ICCEMDAN) to address issues such as signal mode mixing and residual noise signals, further improving the integrity of the decomposed signals [8; 9; 10]. However, these algorithms still suffer from a certain degree of mode mixing and are based on empirical rather than theoretical foundations. [PERSON] proposed Empirical Wavelet Transform (EWT) to address the above issues in 2013. By segmenting the Fourier spectrum of the target signal, it adaptively decomposes complex signals into empirical wavelet functions within specific frequency ranges [11]. However, the research above only analyzed the time-domain part of the signal without considering the frequency-domain part.