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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The vulnerability of civil receivers of the Global Satellite Navigation System (GNSS) to spoofing jamming has raised significant concerns in recent times. Traditional multi-antenna spoofing detection methods are limited in application scenarios and come with high hardware costs. To address this issue, this paper proposes a novel GNSS spoofing detection method utilizing three low-cost collinear antennas. By leveraging the collinearity information of the antennas, this method effectively constrains the observation equation, leading to improved estimation accuracy of the pointing vector. Furthermore, by employing a binary statistical detection model based on the sum of squares (SSE) between the observed value and the estimated value of the pointing vector, real-time spoofing signal detection is enabled. Simulation results confirm the efficacy of the proposed statistical model, with the error of the skewness coefficient not exceeding 0.026. Experimental results further demonstrate that the collinear antenna-based method reduces the standard deviation of the angle deviation of the pointing vector by over 55.62% in the presence of spoofing signals. Moreover, the experiments indicate that with a 1 m baseline, this method achieves 100% spoofing detection. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spoofing attacks have become one of the main threats in global navigation satellite system receiver applications. The maximum likelihood (ML) distortion monitoring, which is a kind of signal quality monitoring (SQM) algorithm, shows good performance in spoofing detection. It employs a multicorrelator structure to measure the correlation peak, and the ML fit residual is derived for spoofing detection. However, ML distortion monitoring requires complex computation and precise code phase search. In this study, we propose a new multicorrelator distortion monitoring algorithm, which omits the process of ML estimation and code phase search. The fit residuals can be directly calculated using the multicorrelator output, achieving better performance than the conventional SQM metrics. Rao test was derived for the modeling of the spoofing detection algorithm, and the detection performance was analyzed mathematically. We employed the Texas Spoofing Test Battery spoofing datasets to evaluate the performance. Experiment results show that the average detection probability is 94.31% at a false alarm rate of 0.01%. The computation of the proposed algorithm is uncomplicated, but needs additional correlator resources. It shows better robustness and detection rate than the previous SQM algorithms. [ABSTRACT FROM AUTHOR]

In this article, we propose a coprime array-based technique that enables global navigation satellite system (GNSS) receivers to reliably defend against spoofing attacks with a small time offset. The technique can not only detect spoofing, but also provide the direction of arrival (DOA) of the spoofing source. Our approach is implemented on the raw digital baseband signals, hence it does not need to perform the complex despreading and acquisition stage of the GNSS receiver. Crucially, the detector is still available when the number of signals (including spoofing and satellite signals) is more than the number of array elements. The simulation results demonstrate the effectiveness of the proposed technique in terms of DOA estimation and spoofing detection. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSSs) are gaining importance as their main applications in localization and navigation particularly for future autonomous vehicle navigation. A drawback of GNSS for spoofing is the simple structure of the GNSS signal and its weak strength on the Earth's surface. These conditions can expose civilian users to GNSS spoofing attacks with the potential to cause significant damage. There are many approaches to detecting GNSS spoofing; one category uses independent sensors, such as inertial sensors. Inertial sensors are typically used as tightly coupled GNSS/INS (inertial navigation system) integration to provide consistency checks for position, velocity, or other parameters. However, in this study, a direct consistency check of the acceleration itself is used to detect spoofing and provide the probability density function (PDF) of a proposed decision variable for a constant-velocity case. The proposed decision variable compares the accelerations of the GNSS and accelerometers for the constant-velocity case. The performance of the proposed spoofing detection method is analyzed based on the detection probability of GNSS spoofing. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) signals have open structure and weak power, which is vulnerable to spoofing attacks. The Innovation-based detection technique has been proved effective for spoofing detection in an integrated navigation system. However, it employs spoofed priori estimate of pseudorange to construct innovation, showing shortcomings like limited detection probability for slowly-varying spoofing attacks, long time to alarm (TTA), and high false alarm probability. This work proposes an enhanced innovation-based spoofing detector via multiple-epoch inertial navigation sensor (INS) prediction. An additional INS unit is added, which is corrected by extended Kalman Filter (EKF) every ${N}$ epoch. Its output is used to replace the spoofed priori estimate in the EKF and then construct an improved innovation. Compared with the conventional innovation, the improved one can be immune to spoofing during multiple epochs and accumulate more abnormal energy due to spoofing attacks. In addition, an innovation bias mitigation method is presented, which exploits the mean of innovation of previous epochs to predict the immediate innovation bias and thus reduces the probability of false alarm. The overall spoofing detection performance is evaluated using both simulation data and hardware-based experiment data collected in Beihang university. A driving test was also carried out to verify its performance in complex urban conditions. Results show that the proposed detector significantly improves the detection performance under slowly-varying spoofing attacks and reduces the probability of false alarm compared with the conventional detector. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global navigation satellite system (GNSS) signal is vulnerable and easily interfered by spoofing because of its opening signal structure and weak signal power. A signal quality monitoring (SQM) technique has been shown to be viable to detect spoofing attacks on GNSS signals. However, the effectiveness of spoofing detection employing a single SQM metric alone is limited and conditional upon the features extracted of that specific SQM metric. The complementary features among various SQM metrics can be exploited to implement joint detection to overcome the deficiency of the individual SQM metric. Motivated by this idea, this paper investigates the multi-metric joint detection technique, which combines various SQM metrics into a composite SQM metric to detect spoofing attacks. This paper proposes two combination strategies, namely amplitude combination mode and probability of false alarm combination mode (PfaM). The overall performance of different metric combinations was verified using simulations and the Texas Spoofing Test Battery dataset. Results show that the PfaM detector outperforms all single SQM metric detectors under various scenarios.

The presence of spoofing signals poses a significant threat to global navigation satellite system (GNSS)-based positioning applications, as it could cause a malfunction of the positioning service. Therefore, the main objective of this paper is to present a spatial-temporal technique that enables GNSS receivers to reliably detect and suppress spoofing. The technique, which is based on antenna array, can be divided into two consecutive stages. In the first stage, an improved eigen space spectrum is constructed for direction of arrival (DOA) estimation. To this end, a signal preprocessing scheme is provided to solve the signal model mismatch in the DOA estimation for navigation signals. In the second stage, we design an optimization problem for power estimation with the estimated DOA as support information. After that, the spoofing detection is achieved by combining power comparison and cross-correlation monitoring. Finally, we enhance the genuine signals by beamforming while the subspace oblique projection is used to suppress spoofing. The proposed technique does not depend on external hardware and can be readily implemented on raw digital baseband signal before the despreading of GNSS receivers. Crucially, the low-power spoofing attack and multipath can be distinguished and mitigated by this technique. The estimated DOA and power are both beneficial for subsequent spoofing localization. The simulation results demonstrate the effectiveness of our method.

Jamming and spoofing are the two main types of intentional interference for global navigation satellite system (GNSS) receivers. Due to the entirely different signal characteristics they have, a few techniques can deal with them simultaneously. This paper proposes a two-stage interference suppression scheme based on antenna arrays, which can detect and mitigate jamming and spoofing before the despreading of GNSS receivers. First, a subspace projection was adopted to eliminate the high-power jamming signals. The output signal is still a multi-dimensional vector so that the spatial processing technique can be used in the next stage. Then, the cyclostationarity of GNSS signals were fully excavated to reduce or even remove the noise component in the spatial correlation matrix. Thus, the signal subspace, including information of the power and the directions-of-arrival (DOAs) of the GNSS signals, can be obtained. Next, a novel cyclic correlation eigenvalue test (CCET) algorithm was proposed to detect the presence of a spoofing attack, and the cyclic music signal classification (Cyclic MUSIC) algorithm was employed to estimate the DOAs of all the navigation signals. Finally, this study employed a subspace projection again to eliminate the spoofing signals and provide a higher gain for authentic satellite signals through beamforming. All the operations were performed on the raw digital baseband signal so that they did not introduce additional computational complexity to the GNSS receiver. The simulation results show that the proposed scheme not only suppresses jamming and spoofing effectively but also maximizes the power of the authentic signals. Nonetheless, the estimated DOA of spoofing signals may be helpful for the interference source positioning in some applications.

The presence of spoofing signals poses a significant threat to global navigation satellite system (GNSS)-based positioning applications, as it could cause a malfunction of the positioning service. Therefore, the main objective of this paper is to present a spatial-temporal technique that enables GNSS receivers to reliably detect and suppress spoofing. The technique, which is based on antenna array, can be divided into two consecutive stages. In the first stage, an improved eigen space spectrum is constructed for direction of arrival (DOA) estimation. To this end, a signal preprocessing scheme is provided to solve the signal model mismatch in the DOA estimation for navigation signals. In the second stage, we design an optimization problem for power estimation with the estimated DOA as support information. After that, the spoofing detection is achieved by combining power comparison and cross-correlation monitoring. Finally, we enhance the genuine signals by beamforming while the subspace oblique projection is used to suppress spoofing. The proposed technique does not depend on external hardware and can be readily implemented on raw digital baseband signal before the despreading of GNSS receivers. Crucially, the low-power spoofing attack and multipath can be distinguished and mitigated by this technique. The estimated DOA and power are both beneficial for subsequent spoofing localization. The simulation results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Jamming and spoofing are the two main types of intentional interference for global navigation satellite system (GNSS) receivers. Due to the entirely different signal characteristics they have, a few techniques can deal with them simultaneously. This paper proposes a two-stage interference suppression scheme based on antenna arrays, which can detect and mitigate jamming and spoofing before the despreading of GNSS receivers. First, a subspace projection was adopted to eliminate the high-power jamming signals. The output signal is still a multi-dimensional vector so that the spatial processing technique can be used in the next stage. Then, the cyclostationarity of GNSS signals were fully excavated to reduce or even remove the noise component in the spatial correlation matrix. Thus, the signal subspace, including information of the power and the directions-of-arrival (DOAs) of the GNSS signals, can be obtained. Next, a novel cyclic correlation eigenvalue test (CCET) algorithm was proposed to detect the presence of a spoofing attack, and the cyclic music signal classification (Cyclic MUSIC) algorithm was employed to estimate the DOAs of all the navigation signals. Finally, this study employed a subspace projection again to eliminate the spoofing signals and provide a higher gain for authentic satellite signals through beamforming. All the operations were performed on the raw digital baseband signal so that they did not introduce additional computational complexity to the GNSS receiver. The simulation results show that the proposed scheme not only suppresses jamming and spoofing effectively but also maximizes the power of the authentic signals. Nonetheless, the estimated DOA of spoofing signals may be helpful for the interference source positioning in some applications. [ABSTRACT FROM AUTHOR]