GNSS Interference Type Recognition With Fingerprint Spectrum DNN Method

• Published in: IEEE transactions on aerospace and electronic systems Vol. 58; no. 5; pp. 4745 - 4760
• Main Authors: Chen, Xin, He, Di, Yan, Xinyu, Yu, Wenxian, Truong, Trieu-Kien
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Bandwidth; Biometric recognition systems; Classifiers; Convolutional neural networks; Deep convolutional neural network (DNN); feature-based expert classifier; Fingerprints; Frequency modulation; Global navigation satellite system; Image classification; Interference; interference fingerprint spectrum; interference-type recognition; Jamming; Neural networks; Object recognition; Receivers
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2022.3167985

It is known that a global navigation satellite system (GNSS) receiver is vulnerable to interference signals, and the interference type recognition, i.e., classification, is helpful for the receiver to sense the spectrum, select an efficient mitigation solution, or identify an interferer. However, conventional interference recognition methods are subject to feature-based expert classification methodologies that are very sensitive to the choice of features and often lack flexibility to different tasks. Recently, some researchers rely on convolutional networks and transform the interference recognition to image classification. Although straightforward, this type of method suffers problems when classifying more complex interference patterns. In this article, a new GNSS interference classifier is proposed, which is composed of a devised interference fingerprint spectrum (FPS) and an especially designed deep convolutional neural network, named as FPS deep convolutional neural network (DNN). The design of the FPS makes different interference signals more discernible. The proposed deep convolutional neural network greatly improves recognition accuracies, especially at low interference powers. The tests show that the average accuracy of the proposed FPS-DNN can reach more than 95% for nine types of interferences at −110 dBm power. This result is significantly superior to the feature-based expert classifier and the conventional convolutional network classifier. It is also demonstrated that the proposed FPS-DNN has a better generalization performance even if the interference is out of the parameter space of the training dataset.