Interference Detection in GNSS Receiver using Adaptive Neural Network based Fuzzy Inference System

Document Type : Original Article


1 Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

2 amin university

3 Department of Information and Communication Technology, Amin Police University, Tehran, Iran


The Global Satellite Navigation System (GNSS) is able to measure the exact time, altitude, longitude, and latitude of any point with high accuracy. The use of this type of navigation in autonomous systems is increasing daily; therefore, the issue of security of these systems is critical. In fact, due to the low update rate and poor power of GNSS signals, receivers are vulnerable to intentional and unintentional interference that can reduce the receiver's accuracy or ultimately impair performance. Therefore, increasing the security of the system and its signal processing has attracted the concentration of industries. In this paper, an adaptive neural network based fuzzy inference system is designed to detect interference in GNSS receivers, which examines two essential criteria for detecting interference in GNSS signals: (1) the distortion of the correlation function, and (2) the received signal power. It classifies the GNSS signals into four groups. Multi-path unintentional interference, intentional spoofing interference, jamming intentional interference, and without disturbance are classified with 59.88, 91.72, 94.33, and 69.50% accuracy, respectively. This system improves the detection accuracy of GNSS signals in multi-path, spoofing, and jamming groups by 3.56, 8.83, and 16.09%, respectively, compared to the previous methods.


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