A Neural Extended Kalman Filter Multiple Model Tracker

A neural extended Kalman filter algorithm was embedded in an interacting multiple model architecture for target tracking. The neural extended Kalman filter algorithm is used to improve motion model prediction during maneuvers. With a better target motion mode, noise reduction can be achieved through a maneuver. Unlike the interacting multiple model architecture which, uses a high process noise model to hold a target through a maneuver with poor velocity and acceleration estimates, a neural extended Kalman filter is used to predict the correct velocity and acceleration states of a target through a maneuver. The neural extended Kalman filter estimates the weights of a neural network, which in turn is used to modify the state estimate predictions of the filter as measurements are processed. The neural network training is performed on-line as data is processed. In this paper, the results of a neural extended Kalman filter embedded in an interacting multiple model tracking architecture will be shown using a high fidelity model of a phased array radar. Six different targets of varying maneuverability will be tracked. The phased array radar is controlled via Level 4 Data Fusion feedback to the Level 0 radar process. Highly maneuvering threats are a major concern for the Navy and DoD and this technology will help address this issue.