Detailed Analysis of the TEXBAT Datasets Using a High Fidelity Software GPS Receiver

Capable and inexpensive Global Positioning System (GPS) spoofers are more likely to threaten our world today due to increased public awareness, advancement of computing power, and the advent of software de- fined radio technology. Just recently, the introduction of GNSS enabled augmented reality games such as Pokemon Go, has also contributed significantly to the global interest in GPS spoofing [1]. To combat this threat, several researchers are developing methods of detecting spoofing attacks [2]. Integral to these efforts are the use of pre-recorded spoofing datasets in order to test the methods being developed. The University of Texas at Austin has published datasets for evaluating spoofing mitigation techniques. These datasets, known as the Texas Spoofing Test Battery (TEXBAT), include eight separate spoofing scenarios. This paper endeavors to offer an addendum to [3, 4] with independent results, observations, and additional commentary regarding the static TEXBAT scenarios as an aid to the community of researchers utilizing this dataset. It is not the intended purpose of this paper to suggest or evaluate anti-spoofing techniques, but rather to inform the community of our observations derived from working with the TEXBAT datasets. This paper leverages an AFIT-developed high- fidelity software-based GPS receiver known as the GNSS Educational Adjustable Receiver Software (GEARS) to process and investigate the TEXBAT spoofing scenarios. This highly flexible and customizable receiver can be used to very quickly explore many different receiver observables. It is capable of subsample sized correlator spacing with carrier-aided code tracking, and utilizes a programmable state machine that dynamically reconfigures the tracking loop parameters to achieve a high degree of flexibility and accuracy [5]. Observations include the characterization of power biases and time offsets between scenarios, the discovery of a “global” code and carrier range rate divergence in some scenarios, and an accurate tabulation of the onset of spoofing in each scenario. Artifacts in the RF spectrum are also described.

[1]  Todd E. Humphreys,et al.  GNSS Spoofing and Detection , 2016, Proceedings of the IEEE.