Towards intelligent communication: machine learning methods for ACARS signal recognition

Abstract

This paper studies the application of machine learning methods for the identification of ACARS (Aircraft Communications Addressing and Reporting System) signals in the field of intelligent communication. We first introduce the characteristics, structure and transmission characteristics of ACARS signals, and then outline the basic knowledge of machine learning, including supervised learning, unsupervised learning and semi-supervised learning. Then, the application of machine learning method in ACARS signal recognition is discussed in detail, including data preprocessing, feature extraction and the application of different algorithms. Through experiments and results analysis, the effectiveness of the machine learning method in ACARS signal recognition is verified, and the challenges and future directions are discussed.