A Dual-Branch CNN-Based Method for Satellite Navigation Jamming Classification and Parameter Estimation

Abstract

The BeiDou-3 Satellite Navigation System (BDS-3) serves as a critical national space-based information infrastructure. However, the signals received at user terminals are characterized by power levels that lie significantly below the noise floor, rendering them highly vulnerable to both intentional suppressive jamming and unintentional interference in complex electromagnetic environments. Traditional anti-jamming techniques predominantly rely on blind suppression strategies. While effective against simple jamming scenarios, these methods suffer from inherent limitations, including low resource utilization and poor adaptability to complex, time-varying interference. To achieve precise suppression and real-time detection of interference, it is necessary to simultaneously perform classification and parameter estimation of the jamming signal. To this end, this paper proposes a multi-task dual-branch convolutional neural network (CNN) learning framework that encompasses four typical types of suppressive jamming: continuous wave interference (CWI), wideband noise interference (WNI), pulse interference (PI), and chirp interference (CI). Taking time-frequency spectrograms of signals as input, the framework achieves accurate identification of interference types and simultaneous estimation of key physical parameters. Simulation and experimental results demonstrate that the proposed method maintains stability and feasibility under low-INR conditions.