Abstract

Due to the influence of transmission path attenuation, Received Global Navigation Satellite System (GNSS) signal is weak, and extremely susceptible to suppression interference, resulting in degradation of signal quality. In order to improve the safety and reliability of the navigation system, the detection and identification of interference signals are very important, and also provides the necessary prior information for the location of interference sources and interference suppression. Traditional interference recognition algorithms are mostly based on manually designed feature parameters such as power spectrums, and preset recognition thresholds to achieve interference classification. The algorithm has high complexity, poor real-time performance, and it is difficult to accurately identify interference types. Aiming at the shortcomings of traditional recognition technology, this paper proposes a GNSS interference source intelligent recognition algorithm, which uses electromagnetic fingerprint features to build a deep convolutional neural network to achieve suppression interference classification. Based on the constructed mathematical model, Pseudo Wigner-Vile (PWVD) electromagnetic fingerprint of interference signal is extracted. By learning different types of interference electromagnetic fingerprint features, the GoogLeNet model cloud realize the real-time identification of the unknown type of interference signal. The experimental results show that compared with the traditional interference signal recognition algorithm, the proposed algorithm has low implementation complexity and greatly improved recognition accuracy. Especially when the interference-to-noise ratio (JNR) is low, thermal noise robustness is stronger.

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