Kalman Filtering and Robust Estimation
Abstract
The objectives of state estimation are to estimate system states from its measured input/output data. This chapter provides a brief discussion of the design of a state estimator for a linear time-invariant plant and derivation of the Kalman filter for a linear time-varying plant using likelihood maximization. Based on this derivation, a robust state estimator is obtained through penalizing the sensitivity of the likelihood function, which is equivalent to that of the innovation process in the Kalman filtering. The resulted robust state estimator has a computational complexity similar to that of the Kalman filter and can also be recursively realized. Convergence conditions for both ...
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