Analysis and implementation of a neural extended Kalman filter for target tracking.
Having a better motion model in the state estimator is one way to improve target tracking performance. Since the motion model of the target is not known a priori, either robust modeling techniques or adaptive modeling techniques are required. The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state-coupling function model and the behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter has also been investigated as a target tracking estimation routine. Implementation issues for this adaptive modeling technique, including neural network training parameters, were investigated and an analysis was made of the quality of performance that the technique can have for tracking maneuvering targets.
Department of Engineering, University of San Diego, San Diego, CA, USA. kramer@sandiego.edu
Kathleen A
KA
Kramer
Stephen C
SC
Stubberud
D003198:Computer Simulation; D005374:Filtration / Q000295:instrumentation; D006801:Humans; D008954:Models, Biological; D008962:Models, Theoretical; D009038:Motion; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors