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Erschienen in:
2018 | OriginalPaper | Buchkapitel
GNSS Spoofing Jamming Recognition Based on Machine Learning
verfasst von : Pan Gao, Songlin Sun, Zhen Zeng, Chenwei Wang
Erschienen in: Signal and Information Processing, Networking and Computers
Verlag: Springer Singapore
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Abstract
While a vast amount of applications and services are based on the Global Navigation Satellite System (GNSS), GNSS needs to deal with jamming, and how to carry out spoofing jamming recognition is the key of achieving high accuracy performance. In this paper, we apply machine learning approaches to GNSS spoofing jamming recognition. In particular, first, we investigate the scheme by employing the classical isometric mapping (ISOMAP) and Laplacian Eigen mapping (LE) algorithm to extract intrinsic feature vector for classification recognition from the original high-dimensional data. Next, we compare this scheme to another two feature vector extraction algorithms developed from principal component analysis (PCA), wavelet transform and singular value decomposition (WT-SVD). Finally, we consider the recognition rates of the four algorithms based on the support vector machine (SVM) classifier, and the effectiveness and the robustness of our scheme are verified via simulations.