## 1 Introduction
With the burgeoning development of deep learning algorithms, the application of neural networks in OFDM signal modulation identification has emerged as a cutting-edge trend [23; 24; 25]. Notable contributions include the proposal of an improved deep neural network (DNN) identification method by [PERSON] et al. [26] and the utilization of long short-term memory (LSTM) neural networks and deep residual networks (ResNet) by [PERSON] et al. to expedite the training process and enhance the accuracy of signal modulation mode identification [27]. Furthermore, [PERSON] et al. [28] have employed a novel approach that abstracts the time-frequency characteristics of modulated signals using the short-time Fourier transform, followed by a conjunction with the instantaneous autocorrelation function to facilitate modulation classification. [PERSON] et al. [29] have leveraged the wavelet transform to abstract signal feature maps, which are subsequently classified using convolutional neural networks (CNNs) [30]. Although deep learning-based modulation identification methodshave the capacity to extract a wealth of nonlinear features from signals [31], their practical application is hindered by the requirement for a substantial number of training samples and prolonged training times. Existing research has the following limitations. Firstly, the majority of LEO satellite monitoring systems rely on spectrum scanning for spectrum analysis, characterized by limited real-time analysis bandwidth. Consequently, burst signals may be overlooked due to scanning durations and probabilistic occurrences. Additionally, real-time spectrum monitoring architectures are constrained by device computational resources and other factors. Secondly, the low orbits; high velocities; and intricate, dynamic channel conditions of LEO satellites directly impact satellite signals, inducing power attenuation and Doppler frequency offset. Thirdly, despite the prevalent use of OFDM signals in LEO satellites, research on the identification of LEO satellite OFDM signals is limited. Current research primarily concentrates on the modulation identification of single-carrier signals. To mitigate these issues, this paper introduces a monitoring methodology tailored for weak prior high-dynamic satellite wideband OFDM signals. Firstly, an architecture for the large-bandwidth real-time acquisition and monitoring of LEO satellite signals is employed. Within this framework, a variable-rate filtering algorithm based on digital resampling is designed, which enables flexible variable sampling rates in an all-digital parallel manner. This approach mitigates computational resource requirements and processing delays. Secondly, a dynamic compensation method leverages ephemeris information to transform the satellite's coordinate system into the geodetic coordinate system, facilitating the extraction of ephemeris information and enabling dynamic compensation for LEO satellite signals. Finally, for LEO satellite OFDM signals, a multidimensional intrinsic feature extraction technique is implemented for joint identification. In summary, we have made the following three main contributions:  
* In response to the challenges posed by the wide bandwidth and high dynamics of signals from Low Earth Orbit satellites, we propose a flexible and adaptive filtering method that enables the parallel real-time acquisition and analysis of broadband signals. The method primarily relies on a dynamically adaptive interpolation filter, which adjusts its parameters in real-time according to the frontend sampling requirements. This architecture ensures that the received signal, after passing through the structure, conforms to a fixed oversampling rate, thereby reducing the complexity of subsequent signal analysis. This approach effectively addresses the complexity and large latency associated with the acquisition and processing of wideband dynamic signals.