Spoofing detection using carrier phase interferometric measurements of inertial satellite navigation systems and machine learning techniques


Аuthors

Veremeenko K. K.*, Zharkov M. V.**, Kuznetsov I. M.***, Pron'kin A. N.****

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia

*e-mail: nio3@mai.ru
**e-mail: mv_zharkov@mai.ru
***e-mail: kuznetsovim@mai.ru
****e-mail: pronkinan@mai.ru

Abstract

The article discusses the problem of detecting intentional interference (spoofing) in integrated inertial-satellite navigation systems. The detection algorithm is based on a technique that determines the direction of reception (direction finding) of signals from global navigation satellite systems, which involves creating an interferometer using at least two receiving antennas. The decision of spoofing in integrated systems presence is based on comparing the direction of signal reception at the frequencies of navigation satellites with the predicted direction of reception calculated using the inertial system and ephemeris data. An approach to replace threshold algorithms with adaptive machine learning techniques is proposed. The binary classification approach choice for identifying the fact of attack is justified. Models based on linear classifiers with Stochastic Gradient Descent training for logistic regression and the Support Vector Machine have been developed, using extended features space such as attitude angles, global navigation satellite systems carrier phase interferometric measurements and their prediction from a strapdown inertial navigation system residuals, as well as their statistical parameters information.

Keywords:

inertial-satellite navigation system, global satellite navigation system interference detection, machine learning, binary classification, linear regression, stochastic gradient descent

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