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A trial of Jammertest 2024 is underway in Norway

Global navigation satellite system (GNSS) jamming and spoofing incidents were becoming more frequent, especially in critical regions such as northern Norway, and could be related to military activities in neighbouring countries. Such security threats have prompted governments and research institutions to seek more jamming-resistant GNSS systems, as well as non-GNSS positioning, navigation and timing (PNT) emergency systems. The Norwegian Ministry of Defence, for example, has proposed the development of PNT-powered alternatives to protect maritime navigation from GNSS interference.

To address this challenge, researchers are evaluating the ability of existing and emerging technology systems to withstand jamming and spoofing attacks in real-world scenarios. During Jammertest 2024, GNSS jamming experiments, including simple and complex spoofing and jamming attacks, are being conducted on the island of An in north-west Norway. These trials are designed to enable participants to identify potential strengths and weaknesses of their GNSS-based systems.

In this area, Septentrio and its Septentrio Mosaic GNSS module are noteworthy.Septentrio is a company that specialises in providing high-precision GNSS solutions, and its Mosaic GNSS module is renowned for its robust anti-jamming capabilities.The Mosaic-X5™ is a superb anti-jamming The Mosaic-X5™ is a superb anti-jamming GNSS receiver that includes an intuitive web-based user interface that makes operation and monitoring easy, allowing you to control the receiver from any device or computer. The home page of the web interface also contains a variety of status indicators for the receiver, making it ideal for monitoring the receiver's operational status.

Principles of interference and spoofing include, but are not limited to, chirp, continuous wave, sawtooth chirp, FM, impulse, narrowband noise, and matched spectrum. There are various methods for detecting these interferences, including statistical-based interference detection methods, time-frequency analysis-based interference detection methods, and machine learning-based interference detection methods.

In order to improve the security and robustness of GNSS, researchers are exploring a variety of technical means. For example, one study proposes a deep learning-based interference detection method, which identifies the type of interference in the signal time-frequency image by training a convolutional neural network (CNN). In addition, there are studies that utilise multi-antenna arrays and spatial processing techniques to detect and suppress spoofed interference.

The concept of an integrated PNT system has also been proposed to enhance the system's immunity to interference. The core of this system is not to rely excessively on GNSS, but to use all available PNT information sources to implement full-airspace target positioning, navigation and timing services. The information sources of the integrated PNT system must be ‘multi-source information based on different physical principles’; the operation and control system should be based on a cloud platform, realising common measurement and control by user volunteers; the user terminals or sensors must be ‘deeply integrated and low-power’; the PNT service information must be ‘intelligent fusion or adaptive fusion’; the PNT service information must be ‘intelligent fusion or adaptive fusion’; and the PNT service information must be ‘intelligent fusion’. ‘intelligently fused or adaptively fused’.

These studies and experiments have shown that the security and robustness of GNSS is a hot spot in current research and that Governments and research institutions are actively developing new technologies and strategies to deal with the threats of interference and spoofing.

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