PROACTIVE GNSS INTERFERENCE MITIGATION AGAINST JAMMING AND SPOOFING USING COMPACT NEURAL MODELS

This paper proposes a proactive approach to GNSS interference mitigation based on compact neural models designed for real-time prediction of interference conditions. Unlike conventional reactive methods, the proposed approach enables forecasting of signal degradation by estimating the probability of interference occurrence, its type (jamming or spoofing), and intensity. The problem is formulated as a time-series modeling task using a sliding observation window, allowing the system to capture temporal dynamics of GNSS signal behavior. The proposed architecture integrates a feature extraction module, a lightweight neural prediction model, and an adaptive control mechanism for proactive adjustment of receiver parameters. The activation of mitigation is governed by a threshold condition applied to the predicted interference probability. A key feature of the approach is its compliance with real-time constraints and suitability for resource-limited GNSS receivers due to the use of efficient neural architectures. The proposed method improves system robustness against radio-frequency interference and reduces positioning degradation under adverse conditions.