Satellite Signal Recognition Based on Deep Approximate Representation Network

The rapid expansion of satellite constellations and the increasing complexity of satellite communication systems have made space situational awareness crucial for ensuring space security and supporting space activities. Among the various communication systems, satellite telemetry signals, particularly those using composite modulation, play a critical role in tracking and managing satellite data. However, recognizing composite modulated signals remains a significant challenge due to the dynamic nature of channel conditions, low signal-to-noise ratios, and the inherent complexity of these signals. In this paper, we propose a novel framework, CLW, for satellite telemetry signal recognition. The CLW framework utilizes a deep approximate representation module combined with adaptive feature aggregation to address these challenges. Specifically, it extracts multiresolution features at different frequency levels from composite modulated telemetry signals, leveraging a channel-space attention mechanism to efficiently aggregate these features. This approach enhances the feature representation quality, reduces the impact of noise and interference, and improves signal recognition accuracy. Extensive simulations and experiments based on high-orbit satellite signal characteristics validate the performance of the proposed CLW framework. The results demonstrate its robustness in handling challenging communication environments and its superior ability to recognize satellite signals compared to existing methods.