This chapter introduces some of the basic techniques of estimation that provide the foundation for state estimation and its applications like tracking and navigation. The problem of parameter estimation is defined and the most commonly used models for unknown parameters (nonrandom and random) are described. The maximum likelihood (ML) and the maximum a posteriori (MAP) estimators are discussed. The least squares (LS) and the minimum mean square error (MMSE) estimators are presented. The various “measures of quality”, unbiasedness and variances of estimators are also included. The consistency of estimators is discussed, together with “information limit” results: the Cramer‐Rao lower bound, the Fisher information, and estimator efficiency. A problem solving section appears at the end of the chapter.
Basic Concepts in Estimation
2002-01-04
31 pages
Article/Chapter (Book)
Electronic Resource
English
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