A GNSS Structural Interference Mitigation Technique Using Antenna Array Processing Saeed Daneshmand #1, Ali Jafarnia-Jahromi #2, Ali Broumandan #3, Gérard Lachapelle#4
# Position Location and Navigation (PLAN) Group, Schulich School of Engineering, University of Calgary 2500 University Drive, N.W., Calgary, Alberta, Canada, T2N 1N4
Abstract— Position solutions provided by Global Navigation
Satellite Systems (GNSS) can be completely misled by structural interference or spoofing threats. An approach utilizing an antenna array is proposed in order to suppress spoofing attacks. The proposed method is based on the assumption that all spoofing signals are transmitted from a single point source. A spatial domain processing technique is proposed to extract the spoofing signal steering vector and consequently to discard the spoofing signals. This method is implemented before de-spreading and acquisition stage of a GNSS receiver. Hence, it does not impose a heavy computational load on the receiver operational process since it does not require any extensive search in the code and Doppler domains to separately despread individual authentic and spoofing signals. Moreover, the proposed method does not require any antenna array calibration process. This pre-despreading interference mitigation technique is further extended to maximize signal-to-noise ratio (SNR) of each individual authentic GNSS signal. Simulation results show that the proposed method effectively countermeasures spoofing attacks for a wide range of received spoofing power.
I. INTRODUCTION Global navigation satellite system (GNSS) signals are
highly vulnerable to in-band interference such as jamming and spoofing. Spoofing is an intentional interfering signal that aims to misdirect GNSS receivers toward generating falsified position/timing solutions [1]. The characteristics of spoofing signals in terms of signal structure and power are very similar to the authentic signals. A spoofing attack is potentially more hazardous than jamming since its target receiver is still providing a position solution which is not essentially its authentic position. Rapid advances in software defined radio (SDR) technology have made GNSS spoofing more flexible and less costly; therefore, GNSS spoofers can be available even for civilian users at a very low cost.
Several anti-spoofing techniques have been proposed in the open literature. Amplitude discrimination, time-of-arrival (TOA) discrimination, consistency cross-check of the solution with inertial measurement units (IMU), polarization discrimination, angle-of-arrival (AOA) discrimination and cryptographic authentication are some of the most popular spoofing detection techniques studied in recent years [1-4].
Spoofing countermeasure using spatial processing is one of the most powerful techniques that have been devised against this threat [5-10]. These techniques mostly rely on the fact that a spoofing transmitter transmits several pseudo random noise (PRN) signals from the same antenna while the authentic signals are transmitted from different satellites and different directions. A multiple antenna spoofing mitigation approach proposed in [6] compares the estimated AOAs of
both spoofing and authentic PRNs to discriminate spoofing signals that are all received from the same direction. In [7,8], spoofing detection techniques taking advantage of synthetic antenna array processing are proposed. These techniques are established based on the fact that all spoofing PRNs experience the same propagation channel and therefore, their corresponding amplitude and phase variations are highly correlated and this correlation can be detected by changing the channel response based on receiver antenna motion. Although these techniques can successfully detect spoofing signals radiated from a single antenna transmitter, they do not provide any processing approach toward discarding the spoofing signals. [9,10] proposed a low computational complexity technique that extracts the spoofing steering vector without the requirement to separately despread authentic and spoofing signals. This method can be implemented before the despreading process and independently of the receiver operation. Hence, it significantly decreases the computational complexity of the receiver process. However, unintentional attenuation of some of authentic signals occurs in the null steering process which may not be tolerable in some GNSS applications.
This paper presents an anti-spoofing technique consisting of two stages, namely a “Projection Matrix Estimation” and an “Optimum Combining for SNR Maximization”. In the first stage, the spoofing signal is discarded by projecting array received signal into a spoofing-free subspace and the next stage maximizes the signal-to-noise ratio (SNR) of each authentic PRN by steering the main lobe of the array beam pattern toward the direction of that signal. The first stage is completely independent of the receiver operation while the second stage operates after the receiver despreading process and it may require some modifications in the structure of conventional GNSS receivers. Simulations are presented to demonstrate the performance of the proposed method under different power levels of spoofing signals. It is observed that the proposed technique successfully discards the spoofing signals. This method not only attenuates the spoofing correlation peaks but also significantly reduces the effect of spoofing cross correlation terms that increase the receiver noise floor.
The paper is organized as follows. In Section II, the system model is introduced. Section III discusses the proposed spoofing mitigation and authentic SNR maximization techniques. In Section IV, simulation results are presented and, finally, conclusions are given in Section VI.
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)
II. SYSTEM MODEL Assume an arbitrary N-element antenna array configuration
in which the distance between each adjacent pair of elements is less than half a GNSS signal carrier wavelength. Without loss of generality, assume that the antenna array is calibrated. Complex baseband representation of the received signal vector at the array for M authentic signals and a spoofing signal (including several PRN codes) can be written as
1 1 1 1N N M M N Nv
× × × × ×= + +r A s b η (1)
where A is a steering vector matrix of satellite signals, b is the steering vector of the spoofing signal and η is a complex additive white Gaussian noise vector with covariance matrix σ2I in which I is a N×N identity matrix. In (1), s is the vector of M GNSS waveforms and v is the spoofing signal waveform. In fact, since authentic PRN signals are coming from different directions, different steering vectors are required in the model whereas all spoofing PRN signals are transmitted from the same direction and therefore they all have the same steering vector.
III. PROPOSED TECHNIQUE The proposed method first calculates a projection matrix
into the spoofing free subspace and then maximizes the SNR for each authentic PRN.
A. Projection Matrix Estimation The spatial correlation matrix of the received signal vector
can be obtained as
{ }.HE=R rr (2)
Considering (1), R can be expressed as 2 2 ,H Hvσ σ= + +R ASA b b I (3)
in which 2vσ is the power of the spoofing signal and S is the
temporal correlation matrix of the authentic signals. Considering the fact that several spoofing PRNs impinge on the antenna array from the same direction, it can be concluded that their corresponding powers are summed up constructively in the space domain while the authentic ones are added non-constructively. In other words, the spatial power density of the spoofing signals is considerably higher than that of authentic signals and therefore, in a scenario with effective spoofing power, b can be estimated by performing the Eigen value decomposition (EVD) of R as
[ ]2 2
2
00
bR b U
U
Hv
H
σ σσ
⎡ ⎤ ⎡ ⎤+≈ ⎢ ⎥ ⎢ ⎥
⎣ ⎦ ⎣ ⎦ (4)
where U is eigenvector of the noise-plus-authentic signals’ subspace with equivalent power of 2σ . Hence, a projection matrix into the reduced-rank spoofing free subspace can be calculated as
.H=P U (5)
Applying this matrix to the received signal vector suppresses the spoofing signal. This may cause attenuation for those authentic signals located in or close to the nulls in the antenna beam pattern; however, the overall positioning performance improves as compared to a single antenna receiver under a spoofing attack [9]. The proposed method does not require array calibration and its computational complexity is low. These features make this method suitable for real-time applications and, therefore, it can be used as an inline anti-spoofing block. However, for those applications in which the authentic attenuation is not tolerable, the proposed spatial filtering can be extended to maximize SNR for individual authentic PRNs. This approach is discussed next.
B. Optimum Combining for SNR Maximization The baseband representation of the received signal for the
mth authentic PRN after despreading and projection can be written as
1 1.m m
N − ×= +r Pa s η (6)
After despreading with a locally generated code corresponding to the mth authentic PRN, only the signal of this PRN is acquired in the correlator outputs. Also due to the projection, the spoofing signals are nullified. In (6), am is the steering vector of the mth authentic PRN and s is the satellite signal after despreading and Doppler removal. η is the complex additive white Gaussian noise vector in the output of correlators. It can be simply verified that the correlation matrix after projection and despreading for the mth authentic PRN can be expressed as
2 2
1 11 1
H Hm m m m N NN N
σ σ− × −− × −
= +R U a a U I (7)
where 2mσ is the power of the mth authentic PRN and 2σ is
noise power at the correlator outputs. The post-despreading noise term consists of ambient noise plus the cross correlation effect of authentic and spoofing PRN signals and, based on the analysis performed in [4], this term can be approximated as a white Gaussian process. The problem of interest is to find the optimal gain vector mh that maximizes the SNR for the mth PRN. This can be obtained using the following maximization:
21,
m
Hm m m
Hm m
Maxσ=h
h R hh h
(8)
which is an eigenvalue problem where mh is the eigenvector corresponding to the largest eigenvalue of mR .
IV. SIMULATION RESULTS Nine authentic and nine spoofing GPS L1 C/A signals were
simulated. The PRN index for authentic and spoofing signals are the same but their corresponding code delays and Doppler shifts are chosen randomly. The average authentic signal power is chosen as -158.5 dBW, the minimum received GPS
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L1 signal power. The power spectral density of ambient noise is assumed to be -204 dBW/Hz. A random code delay and Doppler frequency shift are assigned to each authentic signal. All spoofing signals are transmitted from the same direction with a -153.5 dBW signal power for each PRN. The selected sampling rate is 10 MHz and a three-element antenna array with the circular configuration and half GPS L1 wavelength spacing is employed.
Figure 1 shows the array gain pattern versus azimuth and elevation angles after projection into the spoofing free subspace. A and S stand for authentic and spoofing sources. The proposed anti-spoofing technique significantly attenuates the spoofer signals. This attenuation is about 19 dB in the spoofing direction. After mitigation, the average injected spoofing power is reduced to -172.5 dBW for each PRN and this is much lower than the authentic signal power level. It can be observed that some authentic PRNs such as PRN 1 are attenuated due to the null steering process. This degradation is minimized by employing the SNR maximization stage.
Figure 2 shows the array gain patterns of the antenna array when the SNR of different authentic signals are maximized. The subplots of this figure illustrate the overall antenna gain corresponding to four authentic PRN signals. These beam patterns maximize the SNR of that specific authentic PRN and mitigates the spoofing signal at the same time. In this case, it is observed that the desired authentic signal is amplified compared to the case of a single antenna receiver, while the spoofing signal is considerably attenuated.
In order to show the overall improvement using the proposed method, Monte-Carlo simulations were performed over 1000 evaluations for different spoofing power levels. The transmit direction, the code delay and the Doppler frequency shift of the spoofing and authentic signals were changed during each simulation run. Figure 3 shows the average SNR of the authentic and spoofing signals as a function of the average input spoofing power for both the single antenna and the proposed array processing method. The SNRs are calculated as the ratio of received authentic to spoofing signal powers (after applying the array gain toward their corresponding direction) to the output power of a noise floor estimator that correlates the received signal with a normalized fictitious PRN [11]. A typical detection threshold has also been shown in this figure. In the case of the single antenna receiver, it is observed that the authentic signals’ SNR decreases as the input spoofing power increases. This is due to the receiver noise floor increase due to the cross correlation terms caused by the higher power spoofing signals. At the same time, the SNR of the spoofing signals increases as the power of the spoofing PRNs increases. For example, when the average input spoofing power is -150 dBW, the authentic SNR for the single antenna process is under the detection threshold while the SNR of the spoofing signal is above it. In this case, a conventional GPS receiver will mistakenly acquire the spoofing correlation peak instead of the authentic one. Considering the proposed spatial filtering method, it is
0 100 200 3000
10
20
30
40
50
60
70
80
Array Gain (dB)
5A3A
9A
6A
4A
SSSSSSSSS
Azimuth (degree)
1A
2A
8A
7A
Ele
vatio
n (d
egre
e)
-50
-40
-30
-20
-10
0
Figure 1 Array gain with respect to azimuth and elevation
Figure 2 Overall array gain for different authentic PRNs
Figure 3 Authentic and spoofing SNR variations as a function of average
spoofing power
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observed that as the spoofing power increases the average SNR of the authentic signals almost remains constant while the spoofing SNR is always far below the detection threshold. Hence, the proposed null steering method not only attenuates the spoofing correlation peaks but also significantly reduces the spoofing cross correlation terms that increase the receiver noise floor. It is also observed that after SNR maximization, the average SNR for authentic signals considerably exceeds the SNR level of a single antenna receiver.
The proposed spatial filtering approach steers the beamformer’s null toward a direction with the dominant spatial power. In the absence of a spoofing attack the proposed spatial filtering approach may attenuate the strongest authentic signal if its power is higher than the average power of other authentic signals. In order to evaluate the authentic signals attenuation due to the proposed spatial filtering process, a Monte-Carlo simulation over 1000 runs has been performed. Figure 4 shows the performance of the proposed anti-spoofing technique when no spoofing attack is present. In this simulation, it is assumed that 10 authentic PRN signals are present and the average received power of the first 9 PRNs is -158.5 dBW. This figure shows the output power of the 10th PRN versus its input power for the case of a single antenna receiver and the proposed antenna array processing technique. AOP stands for “Authentic signal Output Power”. In these plots, the input power of the 10th PRN is increasing from -160dBW to -153dBW, which is the maximum received authentic GPS power on earth. It is observed that for the case of a single antenna receiver, the output power linearly increases as the input power increases. However, for the first stage of proposed spatial filtering technique, the output power is slightly lower than the case of a single antenna receiver. However, after applying SNR maximization, the output power of the authentic PRN is always higher than that of the single antenna case.
V. CONCLUSIONS The array processing method proposed herein uses a pre-
despreading approach to extract the spatial characteristics of spoofing signals without acquiring and tracking all the spoofing and authentic PRNs separately. The proposed spatial processing method removes the spoofing correlation peaks and decreases the elevated noise floor caused by the spoofing interference. Furthermore, this method does not require any array calibration, a common burden with array processing techniques. The proposed anti-spoofing method has also been extended to optimally combine the antenna outputs in order to maximize the individual authentic signal’s SNR. These features make it suitable for real-time applications and thus, it can be properly integrated into the next generation of multi-antenna GNSS receivers.
Figure 4 Authentic output power in the absence of a spoofer
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[2] B. M. Ledvina, W. J. Bencze, B. Galusha and I. Miller “An In-Line Anti-Spoofing Device for Legacy Civil GPS Receivers” Proceedings of the 2010 International Technical Meeting of The Institute of Navigation, 25-27 January 2010, San Deigo CA, pp. 698-712
[3] H. Wen, P. Y. Huang, J. Dyer, A. Archinal and J. Fagan “Countermeasures for GPS Signal Spoofing” ION GNSS 18th International Technical Meeting of the Satellite Division, 13-16 September 2005, Long Beach CA, pp. 1285-1290
[4] A. Jafarnia-Jahromi, GNSS Signal Authenticity Verification in the Presence of Structural Interference. PhD Thesis, Department of Geomatics Engineering, University of Calgary, September 2013.
[5] R. G. Hartman and P. Minn Spoofing detection system for a satellite positioning system US Patent 5557284, 1995, 13 pages
[6] C. E. McDowell “GPS Spoofer and Repeater Mitigation System using Digital Spatial Nulling”, US Patent 7250903 B1, 2007, 7 pages
[7] J. Nielsen, A. Broumandan and G. Lachapelle “Spoofing Detection and Mitigation with a Moving Handheld Receiver” in GPS World magazine, vol. 21, no. 9, September 2010, pp. 27-33
[8] M. L. Psiaki, M.L., Powell, S.P., O'Hanlon, B.W., “GNSS Spoofing Detection using High-Frequency Antenna Motion and Carrier-Phase Data,” Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2013), Nashville, TN, September 2013, pp. 2949-2991.
[9] S. Daneshmand, A. Jafarnia, A. Broumandan and G. Lachapelle “A Low-Complexity GPS Anti-Spoofing Method Using a Multi-Antenna Array” in Proceedings of ION GNSS 2012, 17-21 September 2012, Nashville TN, 11 pages
[10] S. Daneshmand, A. Jafarnia Jahromi, A. Broumandan, J. Nielsen and G. Lachapelle GNSS Spoofing Mitigation in Multipath Environments Using Space-Time Processing, Proceedings of the European Navigation Conference (ENC2013), 23-25 April 2013, Vienna.
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2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)
