Anti-Jamming techniques for aviation GNSS-based navigation systems: Survey

Issam, Senhaji.Mouhaddib; Addaim, Adnane; Madiabdessalam, Ait

doi:10.1109/icecocs50124.2020.9314449

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…Especially in the context of global navigation satellite systems (GNSS), intentional interference such as jamming and spoofing has been on the rise in recent years and can have significant adverse effects on the navigation performance of GNSS receivers, as discussed for example in [ 11 , 12 , 13 , 14 , 15 ].…”

Section: State-of-the-art-review and Paper Contributionsmentioning

confidence: 99%

“…Future aviation applications, and in particular unmanned aerial vehicles (UAVs), will increasingly rely on GNSS-based navigation and positioning solutions [ 14 , 15 ]. Safety-critical applications, such as those from the aviation domain, require a high capability of anti-spoofing and anti-jamming detection, or, in other words, a high identification accuracy of genuine and malicious transmitters.…”

Section: State-of-the-art-review and Paper Contributionsmentioning

confidence: 99%

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A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Wang

Sánchez

Caparra

et al. 2021

Sensors

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Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.

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Section: State-of-the-art-review and Paper Contributionsmentioning

confidence: 99%

Section: State-of-the-art-review and Paper Contributionsmentioning

confidence: 99%

A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Wang

Sánchez

Caparra

et al. 2021

Sensors

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“…A comprehensive survey of intentional RFI and countermeasures, including detection, localization, classification, and mitigation, over the last four decades can be found in [ 13 ]. For an overview over the last decade, the interested reader can refer to [ 13 , 14 , 15 , 16 , 17 ] for spoofing and [ 18 , 19 , 20 , 21 ] on jamming.…”

Section: Introductionmentioning

confidence: 99%

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

Troglia Gamba,

Polidori,

Minetto

et al. 2024

Sensors

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The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector.

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“…10 In both cases, jamming disrupts the reception of the legitimate signals and obstructs the attacked communication link. 8 In fact, jamming poses a serious challenge to wireless communication systems and networks, for example, internet of things, e-health, 11 satellite-based navigational systems, 12 smart cities, and smart homes. 13 Furthermore, jamming techniques are rapidly evolving which makes them difficult to mitigate.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based anti‐jamming technique at the physical layer

Chehimi

Awad

Al‐Husseini³

et al. 2023

Concurrency and Computation

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The reliance on wireless services to exchange critical data is associated with various threats and attacks, which must be mitigated to ensure integrity and security of those wireless services. Posing a serious challenge to wireless systems, jamming is among these attacks. In order to mitigate jamming, directive antennas are used to minimize the signals that are received from the jammer, while maximizing the received legitimate signal from the authorized transmitter. In this paper, we propose a machine learning‐based anti‐jamming framework to provide a spatially dynamic and instantaneous anti‐jamming performance that is achieved at the physical layer. The proposed framework incorporates a dataset that can be deployed in the hardware of a receiver with a massive Multiple‐Inputs Multiple‐Outputs–(MIMO) antenna. Our extensive performance evaluation results demonstrate the effective performance of the proposed framework in preserving integrity of a massive–MIMO communication system despite the presence of a hostile jammer. Particularly, due to the tabular nature of the generated dataset, tree‐based random forest models achieved the best performance with a signal‐to‐interference‐plus‐noise ratio accuracy of 92%$$ 92\% $$ and fast anti‐jamming response in around 1.66 s under sever jamming conditions.

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Anti-Jamming techniques for aviation GNSS-based navigation systems: Survey

Cited by 13 publications

References 11 publications

A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

Machine learning‐based anti‐jamming technique at the physical layer

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