NEKF IMM tracking algorithm
Mark W. Owen, Allen Roger Stubberud · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Highly maneuvering threats are a major concern for the Navy and the DoD and the technology discussed in this paper is intended to help address this issue. A neural extended Kalman filter algorithm has been 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 corrections to the 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 are 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 simulation results of a tracking problem using a neural extended Kalman filter embedded in an interacting multiple model tracking architecture are shown. Preliminary results on the 2nd Benchmark Problem are also given.