The SPOTME (Spoof-Detection and Location Estimation using Deep Learning) project addresses the growing number of GNSS interference and spoofing attacks threatening both military operations and civilian critical infrastructures. By combining classical signal-processing methods with advanced deep-learning approaches, SPOTME aims to detect, classify, and mitigate threats such as jamming, spoofing, and meaconing in real time.

A scalable AI platform will be developed to enhance the resilience and authenticity of GNSS information across defence and civilian domains. The project applies Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), together with hybrid physics-informed models to ensure robust and explainable detection results.

Beyond defensive capabilities, SPOTME explores offensive simulation and red-team testing to analyse deception scenarios and improve system robustness. Explainable-AI components and intuitive user interfaces will support rapid and transparent decision-making in critical missions.

SPOTME contributes directly to the Austrian Ministry of Defence’s AI Strategy, reinforces Austria’s technological sovereignty in navigation warfare, and supports the implementation of national AI solutions. The results are transferable to civil sectors such as energy, transport, and logistics.