A Novel Anti-DRFM Active Jammer Method for Two-Station Netted Radar Based on Extended Kalman Filtering

Abstract

Active jammers in the form of digital radio frequency memory (DRFM) could produce false targets with a coherence of range and velocity, making it increasingly efficient in interrupting radar systems based on data correlation analysis. By integrating the temporal variability characteristic and mitigating the impact of measurement errors inherent in the radar system, we propose a novel false target recognition methodology for a two-station netted radar utilizing extended Kalman filter (EKF) and the Neyman–Pearson criterion. From the aspects of filtering characteristics and active jammer parameters, the simulation shows that the technique could obtain observable different convergence curves for false targets and is sensitive to velocity or range deception under specific parameters. Moreover, the method exhibits a favorable tolerance for measurement error and an exceptional capability to identify false targets from various directions, considering both radar performance parameters and spatial scale. This outcome highlights the significance of setting specific parameters following countermeasure scenarios and requirements, showcasing its practical value in engineering practice.