NUST Institutions Library Catalogue

The Global Navigation Satellite Systems (GNSS) is a low cost (to user), globally available,
highly precise, all-time/all-weather timing and positioning system. Because of these advantages,
its usage is growing at a very high rate. The GNSS system is unique in several respects
including their low transmit power, open structure, and simplex (receive-only user systems)
operation. Due to the fact that GNSS signals are received with very low power, they are
prone to interference events that may reduce the usage or decrease the accuracy. Also due
to the open signal structure, the receivers are prone to intentional interference to deceive the
user, known as the spoofing attack.
Spoofing of GNSS receiver poses great risks and challenges due to the user stake involved
and the damage it can cause. A low cost GNSS receiver under spoofing attack usually does
not alert the user and could mislead to a hazardous situation. However, the attack has its own
limitations and shortcomings that are exploited in the anti-spoofing receivers. These include
changing the lock of the receiver from authentic to the spoofing signal, spatial diversity of
the authentic signal, and the coherency between the code, carrier and navigation message
stream.
In the efforts of mitigating the effects of spoofing on a user receiver, the detection is usually
the foremost task. Therefore, the focus of this thesis is on the design and validation of
different signal processing techniques that aim at detection of the spoofing attack effects.
Three different techniques are thesisized. These techniques include slope metric based detector,
PCA based detector, and ACF shape distortion based detector. The goal of slope
based spoofing detection technique is to measure the signal quality by analyzing the slope
of ACF. The formulation of slope-based metrics involves monitoring correlators along with
tracking correlators in the receiver’s channel, to capture the slope correctly. The PCA based
technique discussed in this thesis, can classify the received signal as clean, multipath and
spoofing through a Baye’s multi-hypothesis classifier constructed from a Monte-Carlo simulation
of the parameters for a receiver operating in a threat environment. The classifier uses
the observation vector consisting of PCA normalized reconstruction error and principal direction
deviation. The ACF shape distortion method compares the measured and the typical
correlator tap values. The measurement is done through multiple correlator taps. The typical
ACF values used are calculated considering the un-spoofed situations in which the signal
contains Line of Sight (LOS) and Non-LOS components and noise.
The detection techniques are checked using simulations, synthetic data and the TEXBAT data shared by the University of Texas, Austin. The methods found to be very effective in
detecting the spoofing attack during the pull-off phase of the attack. In each case, Different
formulations of the proposed methods are studied for optimal detection.
The thesis also discusses mathematical analysis of the noise as the signal travels in different
parts of the receiver. The analysis primarily investigates the joint statistics of the signals
at the output of the correlators. The results of the analysis are useful in the development of
a detector that utilizes the statistical correlation between the correlator outputs such as PCA
based detectors.
The thesis also discusses a method of generating spoofing signals using two un-spoofed
recordings done simultaneously in open sky conditions. The resultant spoofing signal can be
used in the validation of anti-spoofing methods, as an affordable alternative to the complete
spoofing equipment.