Discrimination of exo-atmospheric active decoys using acceleration information

Range deception is a common electronic countermeasure technique used for ballistic missile penetration. The well-designed decoys of range deception can even form stable tracks. Discrimination of these decoys is difficult and one potential way is at radar data processing level by using motion features. This study presents a novel method, called acceleration matched discrimination algorithm, which fully utilises the fact that the accelerations of exo-atmospheric active decoys are essentially different from that of physical targets (e.g. warhead), that can discriminate these decoys at the radar data processing level. First, the acceleration model of active decoys is explicitly derived. Secondly, the acceleration matched coefficient (AMC) is defined based on the filtered acceleration and theoretical acceleration. By employing the extended Kalman filter, the instantaneous variance of AMC is also derived. Finally, the discrimination algorithm is designed based on a batch-processing weighted least squares estimate and its estimated variance. Theoretical analysis and simulations indicate that the discrimination method is valid and feasible. Furthermore, the discrimination performance analysis due to the influence of radar position, radar measurement error and data rate are also covered.