Joint Pulse Repetition Interval and Scan Pattern‐Based Time‐of‐Arrival Prediction Using Machine Learning

In electronic warfare (EW) systems, accurate time‐of‐arrival (TOA) prediction for radar signals is critical for effective jamming. TOA depends on both pulse repetition interval (PRI) and radar scan patterns, which are increasingly complex due to technological advancements. Unlike prior research focusing solely on one factor, this paper proposes a machine‐learning model that leverages both PRI and scan patterns to predict subsequent radar pulse TOA. The system demonstrates superior prediction accuracy and robust performance in noisy environments and under varying probabilities of detection (POD). This is achieved by separating the PRI sequence and the radar scan interval, an approach that can be applied to different system designs. The proposed method applies a filtering algorithm that separates PRI and scan sequences, feeding them into distinct LSTM models, with a splitting technique addressing missing pulses. Importantly, the model integrates the radar antenna main lobe and side lobe information to enhance jamming effectiveness. Simulation results also demonstrate that the main design concept—considering both PRI and scan type—can be used for different techniques, such as a decision tree. This approach significantly improves TOA estimation, handles diverse radar patterns and represents a valuable contribution to radar technology for improved situational awareness and operational efficiency.