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- W1574183849 abstract "This chapter discusses extensions of basic linear least - squares techniques, including constrained least - squares estimation, recursive least squares, nonlinear least squares, robust estimation, and measurement preprocessing. Nearly all physical systems are nonlinear at some level, but may appear linear over restricted operating ranges. Simple minimum/maximum constraints on individual states are probably the most common type of inequality constraint. The conditional mean or minimum variance solution to the Bayesian least - squares problem provides the recursive algorithm this is the measurement update step of the Kalman filter. Recursive least squares can also be implemented using the MGS version of the QR algorithm. Robust estimators are designed to be insensitive to small errors in assumptions or models, particularly with respect to non - Gaussian measurement error distributions. Controlled Vocabulary Terms estimation theory; Kalman filters; least squares approximations; nonlinear filters" @default.
- W1574183849 created "2016-06-24" @default.
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- W1574183849 date "2011-02-14" @default.
- W1574183849 modified "2023-10-17" @default.
- W1574183849 title "Least-Squares Estimation: Constraints, Nonlinear Models, and Robust Techniques" @default.
- W1574183849 doi "https://doi.org/10.1002/9780470890042.ch7" @default.
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