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Spoofing attacks are a serious problem for civil GNSS applications with safety content, such as airplane landing or maritime navigation in harbors. Also many strategically important infrastructures, such as electric power grids or mobile communications networks, are becoming increasingly dependent on GNSS services. Military GNSS users solve that problem by signal encryption at chip level. This reduces the threat to only allow for meaconing, i.e. retransmitting the GNSS signals from a certain location, since the exact waveform is unpredictable. Civil users cannot rely on encryption at the moment and most likely in the near future. They must be protected by additional techniques, which are able to detect and mitigate spoofing attacks. A number of receiver-autonomous solutions for the spoofing problem have been proposed in the last decade. For single antenna receivers the detection of spoofing attacks can rely on the observation of the time evolution of different signal parameters such as power and Doppler frequency shift, the PRN code delay and its rates, the correlation function shape as well as the cross-correlation of the signal components at different carrier frequencies. However, the most advanced protection against the sophisticated spoofing attacks can be provided by utilizing the spatial domain for signal processing available by using antenna arrays ([1], [2], [3], [4], [5]). A GNSS receiver with an antenna array is able to estimate the directions of arrival of the impinging waveforms and so to discriminate between the authentic and counterfeit signals. Moreover the malicious signals can be mitigated by generating a spatial zero into the array antenna reception pattern in the direction of the spoofing source(s). The use of the array-aided joint estimation of the array attitude and spoofing detection was investigated by the authors in [1], [3], [5]. A post-correlation estimation of the signal direction of arrival (DOA) was utilized as the first step of the corresponding signal processing chain. This approach however still suffers from the effects of short-term distortions in the receiver tracking loops and the resulting unavailability of the DOA estimations during the spoofing attack. Two approaches have been identified to overcome this effect. On the one hand, a more accurate direction of arrival detection and antenna calibration can be used. On the other hand, the attitude estimation can be made more robust by skipping the DOA estimation step and using instead directly the post-correlation array outputs in the underlining measurement model, similar to method 2 in [6]. The latter possibility will be exploited throughout the current paper. One of the main challenges here is to design robust and computationally effective attitude estimation when the post-correlation array outputs consist of the superposition of the authentic and counterfeit signals. This problem, for example, is not adequately handled in [6] and [7]. In the aforementioned approaches, the estimation of the actual direction of arrival in terms of (antenna local) azimuth and elevation was done explicitly before the attitude was estimated. The approach presented in the paper will avoid this (computationally expensive) step, by introducing an adequate measurement model. This model connects the measured relative phases between the antennas elements (spatial signature) to the ones expected from the almanac. This interconnection involves the receiver attitude, which is the state to be estimated. In a second step, the model fit (i.e. residuals of least square fit) is used to detect anomalies. Further processing is done by comparing the spatial signature for different satellites. Contrary to using the cyclic nature of PRN codes to detect the direction in the pre-correlation domain as described in [2], the spatial signature in the post-correlation domain is used. If one dominant direction is present, the likelihood of spoofing or meaconing is considered high. If detected, a second processing stage is triggered, capable of spatially filtering out the spoofers signature (post-correlation nulling). Finally a second run of the aforementioned procedure is done to estimate the antennas attitude using a spatially filtered signal. Theoretical results as well as hardware simulations ([8]) show, that if a GPS/CA or Galileo receiver already tracks a certain PRN, the likelihood of success is very low for an unsynchronized spoofer. In this context (un)synchronized is related to the PRNs current frequency shift (caused by the Doppler Effect), as well as code delay. The code delay error should not be larger than one chip in general. The tolerable frequency mismatch however, highly depends on the receivers implementation (i.e. FLL and PLL parameters and stages), but should not be bigger than a few multiples of 50 Hz. A synchronized spoofer or meaconing signal which is turned on when the receiver already tracks the corresponding PRN will be considered in the context of the paper. The described methods will be evaluated using software simulations. Scenarios without spoofing or meaconing are used to demonstrate the attitude estimation. Scenarios with repeaters will be used to demonstrate the two-stage approach with spatial filtering.  [1] M. Meurer, A. Konovaltsev, M. Cuntz, and C. Hattich, “Robust Joint Multi-Antenna Spoofing Detection and Attitude Estimation using Direction Assisted Multiple Hypotheses RAIM,” in Proceedings of the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2012), September 2012, Nashville, TN, USA., 2012. [2] S. Daneshmand, A. Jafarnia-Jahromi, A. Broumandon, and G. Lachapelle, “A low-complexity GPS anti-spoofing method using a multi-antenna array,” in Proc. ION GNSS 2012, 2012, pp. 1233–1243. [3] A. Konovaltsev, M. Cuntz, C. Haettich, and M. Meurer, “Autonomous Spoofing Detection and Mitigation in a GNSS Receiver with an Adaptive Antenna Array,” in Proc. ION GNSS+ 2013, 2013, p. 12. [4] M. Appel, A. Konovaltsev, and M. Meurer, “Robust Spoofing Detection and Mitigation based on Direction of Arrival Estimation,” in Proc. ION GNSS+ 2015, 2015, pp. 3335–3344. [5] M. Meurer, A. Konovaltsev, M. Appel, M. Cuntz, E. M. Meurer, A. Konovaltsev, M. Appel, and M. C. De, “Direction-of-Arrival Assisted Sequential Spoofing Detection and Mitigation,” in ION ITM 2016, 2016. [6] M. Markel, E. Sutton, and H. Zmuda, “An antenna array-based approach to attitude determination in a jammed environment,” in Proceedings of the 14th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2001), 2001, pp. 2914–2926. [7] S. Daneshmand, N. Sokhandan, and G. Lachapelle, “Precise GNSS Attitude Determination Based on Antenna Array Processing,” in Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation, ION GNSS+ 2014, Tampa, Florida, September 8-12, 2014, 2014. [8] M. Appel, A. Hornbostel, and C. Haettich, “Impact of meaconing and spoofing on galileo receiver performance,” 7th ESA Workshop on Satellite Navigation Technologies NAVITEC, 2014.

proceedings of the satellite division's international technical meeting (online)/proceedings of the satellite division's international technical meeting (cd-rom)

Joint Antenna Array Attitude Tracking and Spoofing Detection Based on Phase Difference Measurements