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- W593524076 abstract "In this chapter, we present an overview of estimation and failure detection for linear systems. In Section 2.2, we discuss linear filtering and its past and current development. First, Section 2.2.1 gives a brief description of the Wiener and Kaiman filters, the most commonly used filters for dynamic systems. Only the basic results are summarized in the section; while the derivation and other topics are treated in Appendix A. Next, in Section 2.2.2, we briefly discuss problems that are more general than linear least squares estimation, including robust filtering, which is one of our main concerns in subsequent chapters. We then motivate the need for robust filters in Section 2.2.3 by demonstrating the deleterious effect of model uncertainties on the Kaiman filter. A formulation of the robust filtering problem is given in Section 2.2.4. The small gain theorem, which is the key to solving that problem, is also introduced in that section. In Section 2.2.5, we describe how the small gain theorem is used in robust estimation. The actual derivation of the filters is left to Chapter 3. Further discussion on robust estimation is found in Section 2.2.6. The robust filtering problem is formulated in Section 2.2.4 in a deterministic context. In Section 2.2.7, we introduce the risk sensitive estimation problem, a stochastic version of robust filtering." @default.
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- W593524076 date "1998-01-01" @default.
- W593524076 modified "2023-09-26" @default.
- W593524076 title "Estimation and Failure Detection: An Overview" @default.
- W593524076 doi "https://doi.org/10.1007/978-1-4471-1586-1_2" @default.
- W593524076 hasPublicationYear "1998" @default.
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