R. Lynn Kirlin

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The standard optimum Kalman filter demands complete knowledge of the system parameters, the input forcing functions, and the noise statistics. Several adaptive methods have already been devised to obtain the unknown information using the measurements and the filter residuals. Methods which a re designed to simultaneously estimate the input forcing functions(More)
Target tracking with Kalman filters is hampered by target madeuvering and unknown process and measurement noises. We show that moving data windows mag be used to analyze state and measurement error sequences, determining robust estimates of bias and covariance. For steps in the system forcing functions and non-Gaussian measurement errors, the robust(More)