An Efficient Way for Satellite Interference Signal Recognition Via Incremental Learning

In satellite communication systems, it is necessary to implement fast and effective interference suppression for unknown interference signals to ensure the safety and reliability of satellite communication, and successful recognition of the types of interference is a prerequisite for ensuring high-efficiency anti-interference. In this paper, the incremental learning method is introduced into the process of satellite interference signals recognition to lower the hardware conditions required by the machine learning algorithm, reduce the memory consumption and shorten the computing time. Firstly, a variety of frequency domain features are extracted from five kinds of interference signals that often appear in satellite communication systems as classification feature parameters. Secondly, the incremental support vector machine learning model proposed by Gert Cauwenberghs et al. is extended to a multiclass model by one-versus-one, decision tree and directed acyclic graph respectively. Then the three improved models are used to train the characteristic parameters of the interference signals to obtain the recognition results. Simulation experiments show that compared with the conventional multi-category support vector machine model, the proposed models have little difference in recognition accuracy, but the computing time and the memory occupation of the training models are greatly reduced. These results are consistent with the actual requirements of satellite communication systems.