RF fingerprint extraction for GNSS anti-spoofing using axial integrated Wigner bispectrum

Spoofing interference has caused serious security problems in global navigation satellite systems (GNSS), so identifying spoofing signals from genuine signals is the key task of GNSS. For the spoofing detection, a novel feature extraction method using axial integrated Wigner bispectrum (AIWB) is investigated in this paper. Compared with diagonal sliced Wigner bispectrum (SWB), this method not only reduces the dimension of the Wigner bispectrum, but also utilizes much more signal information. We use singular values of AIWB as the feature vector of the signal, and use support vector machine (SVM) to realize the identification of spoofing signals. Compared with SWB and bispectrum, the simulation results demonstrate that AIWB has lower Equal Error Rate (EER), especially in relatively low SNRs, and has better recognition performance due to its lower mean misclassification rate and smaller variation.