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—Multiple-model approach provides the state-of-the-art solutions to many problems involving estimation, filtering, control, and/or modeling. One of the most important problems in the application of the multiple-model approach is the design of the model set used in a multiple-model algorithm. To our knowledge, however, it has never been addressed… (More)

Many estimators and filters provide assessments (e.g., MSE matrices) of their own estimation errors. They are, however, obtained based on simplifying assumptions that are not necessarily valid. Then the questions are: Are these self-assessments trustable? How trustable are they? We referred to these problems as the credibility of the estimators/filters.… (More)

Most estimators and filters provide assessments of their own estimation error, often in the form of mean-square error. Are these self-assessments trustable? What is the degree to which they are trustable? This is Part I of a two-part series that provides answers to some of these questions, referred to as the credibility of the estimators. It formulates the… (More)

Many estimators and filters provide assessments of their own estimation error. Are these self-assessments trustable? What is the degree to which they are trustable? This is Part II of a two-part series that provides answers to some of these questions , referred to as the credibility of the estimators. It proposes several tests for credibility and a… (More)

—The ability to meaningfully assess performance is crucial for understanding, developing and comparing estimators. The optimality of an estimator relies on estimation criterion and there exists a significant gap between estimation criterion and application requirements, so the estimation criterion is not go for evaluating or comparing algorithms. Different… (More)

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