Brief paper: A parametric programming approach to moving-horizon state estimation
Automatica (Journal of IFAC)
Multi-sensor calibration through iterative registration and fusion
Computer-Aided Design
A survey of data smoothing for linear and nonlinear dynamic systems
Automatica (Journal of IFAC)
On accurate localization and uncertain sensors
International Journal of Intelligent Systems
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This discussion is directed to least-squares estimation theory, from its inception by Gauss1 to its modern form, as developed by Kalman.2 To aid in furnishing the desired perspective, the contributions and insights provided by Gauss are described and related to developments that have appeared more recently (that is, in the 20th century). In the author's opinion, it is enlightening to consider just how far (or how little) we have advanced since the initial developments and to recognize the truth in the saying that we ``stand on the shoulders of giants.''