Impact time and point prediction: an analysis of the capabilities of a neural extended Kalman filter
Stephen Craig Stubberud, Kathleen Ann Kramer · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Part of target tracking is the use of existing information to predict behavior. Often, this part of the tracking algorithm is used for data association and the update step of the fusion process as well as, recently, to guide weapons to the target. Another application is to estimate the point of impact of enemy ballistic munitions. The determination of the actual threat posed by enemy position permits prioritization of targets for response and can limit the need to expose friendly units. The flight trajectory of ballistic ordinance, while theoretically understood, can be affected by a number of unknown factors, such as air turbulence and drag. To accurately predict the projectile's flight path, an adaptive Kalman filter approach is proposed to track and predict the target trajectory. The Kalman filter uses a neural network as an on-line function approximator to improve the motion model of the target tracking. The neural network is trained in conjunction with the tracking algorithm. Both track states and neural network weights use the same residual information. The neural network model is added to the motion model and provides an improved track prediction of a mortar impact time and its location. Analysis of the approach's performance is compared to varying tracking systems with different levels of target motion accuracy. Two a priori motion models for the neural approach are applied: a straight-line motion model and a basic ballistic trajectory. The results show the ability of the technique to provide accurate target predictions with limited a priori information.