## 1 Introduction  
Satellite communication network systems are poised to become a vital component of future global networks, as well as integral to the development of 5G and 6G technologies [1, 2, 3]. Contemporary Low Earth Orbit (LEO) satellite internet primarily employs broadband OFDM signals for communication within dynamic channels [4, 5, 6]. Ensuring the proper functioning of LEO satellite communication systems necessitates addressing the critical challenge of monitoring the OFDM signals of the dynamic channel environment of LEO satellites. Presently, LEO communicate signals mainly use the Ka band (20-30 GHz) and Ku band (10-18 GHz). These millimeter-wave signals exhibit line-of-sight propagation traitsand are acutely susceptible to atmospheric attenuation. Characterized by their dynamic nature and pronounced Doppler frequency shifts, LEO satellite channels, due to their lower orbits and higher velocities, necessitate dynamic compensation mechanisms [7; 8; 9]. The signal bandwidth of LEO constellations is rapidly expanding, with an average bandwidth extending up to 800 MHz (with plans for 2 GHz bandwidth signals), necessitating real-time monitoring and signal analysis capabilities that can accommodate ultra-wide bandwidths. Existing monitoring systems are constrained by hardware limitations, with real-time analysis bandwidths typically spanning from 40 MHz to 160 MHz, sufficient for the analysis of signals from a single transponder. For the comprehensive analysis of multi-transponder, multi-signal, or wide bandwidth spectra, cyclic scanning and time-sharing analysis methods are employed, albeit with the inherent risk of missed detections due to the probabilistic nature of cyclic scanning [10; 11; 12]. To circumvent these limitations, ultra-wideband monitoring receivers and antenna systems are imperative, possessing the following critical performance attributes: an ultra-wideband reception spectrum, encompassing at least the Ka and Ku bands and extending to at least 30 GHz; ultra-large real-time bandwidth, necessitating devices capable of achieving at least 500 MHz in real-time; rapid frequency scanning, demanding swift and uninterrupted scanning across the entire frequency range to capture satellite signals; and high dynamic range, accommodating strong signals in close proximity and weak signals at greater distances [13]. Currently, in the domain of satellite signal detection and identification, research has predominantly been directed towards the modulation recognition of single-carrier signals, with a paucity of studies addressing the identification of LEO satellite OFDM signals. Feature-based modulation recognition methods and likelihood ratio-based modulation recognition methods are the predominant techniques employed. Likelihood ratio-based methods, while achieving optimal identification performance [14], are encumbered by high computational complexity and poor robustness. Consequently, feature-based methods have gained widespread adoption due to their low computational complexity, high identification efficiency, and robust performance, spurring extensive research efforts. Feature-based modulation identification methods encompass a myriad of approaches for extracting modulation mode recognition feature parameters, including the utilization of digital signal time-frequency characteristics [15], higher-order cyclostationary statistics [16], algorithms predicated on cyclic spectrum [17], and algorithms based on autocorrelation [18]. Furthermore, should the signal inadvertently forfeit its circularity as a consequence of the dynamic nature of the Low Earth Orbit satellite channel, the application of the Widely Linear Estimation technique would be appropriate for the subsequent analysis of the signal characteristics [19]. However, these studies have not adequately accounted for the actual channel conditions of LEO satellite communication, rendering the extracted features inapplicable to actual channel variations [20; 21; 22].