An adaptive filtering approach to target tracking
Venkatesh Madyastha, Anthony J. Calise · 2005
A method is presented for augmenting an extended Kalman filter with an adaptive element. The resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics. The design of the adaptive element employs a linearly parameterized neural network. The network weights are adjusted on line using the filter error residuals. Boundedness of signals is proven using Lyapunov's direct method and a backstepping argument. Simulations illustrate the theoretical results.