1 GNSS Interference Identification beyond Jammer Classification

Classification of jamming signals in Global Navigation Satellite System (GNSS) has been explored recently using machine learning including Support Vector Machine (SVM) and Convolutional Neural Network (CNN) techniques. Identification of the jammer types helps to choose preferred methods which are more effective to remove such jammer. For example, adaptive frequency and time-domain filtering methods are commonly used for continuous-wave (CW) jammer mitigation; frequency-domain finite impulse response (FIR) or infinite impulse-response (IIR) filtering technique can put a notch in the jamming frequency. However, these techniques need primary information about jamming signal structure. Besides jamming, other interferences also cause receiver performance degradation including spoofing and obstructions in nearby environment such as mountains or buildings. This paper identifies these types of interferences besides the jammer types. Practical issues such as fading channels, Doppler frequencies, and phase shifts are considered for the satellite, jammer, and spoofer links.