Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization

Version 1 : Received: 22 May 2020 / Approved: 23 May 2020 / Online: 23 May 2020 (05:10:36 CEST)

A peer-reviewed article of this Preprint also exists.

Ujan, S.; Navidi, N.; Landry, R.J. Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom. Appl. Sci. 2020, 10, 4608. Ujan, S.; Navidi, N.; Landry, R.J. Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom. Appl. Sci. 2020, 10, 4608.

Journal reference: Appl. Sci. 2020, 10, 4608
DOI: 10.3390/app10134608

Abstract

Satellite communication (Satcom) is an artificial geostationary satellite that facilitates a wide range of telecommunications. Considering its quality of service (QoS) and security is crucial in government/military applications. The most challenging situation for efficient Satcom is radio frequency interference (RFI) environment. Thus, it is necessary to ensure that transmissions are incorruptible or at least sense the quality of its spectrum. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron neural network. We consider signal modulation and the type of RFI as the characteristics of a real-time video stream transmitted in the direct broadcast satellite. Four different modulation types are investigated in this study. Moreover, the combination of the communication signal with various kinds of interference and their effects on the classification method widely have been analyzed. Besides, two robust feature selection techniques have been developed to reduce the data-set dimensional, which leads to optimizing the classification process. The results show that the Genetic Algorithm (GA) slightly outperforms Principal Component Analysis (PCA) for feature selection. Furthermore, the robustness of the proposed techniques is assessed to detect unknown signals at different signal to noise ratios.

Subject Areas

Supervised Learning; Time Series Classification; Jamming Detection; Automatic Modulation Classification; Feature Selection; Genetic Algorithm; Principal Component Analysis; QPSK modulation; APSK modulation

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