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In tracking applications, target dynamics are usually modeled in the Cartesian coordinates, while target measurements are directly available in the original sensor coordinates. Measurement conversion is widely used such that the Kalman filter can be applied in the Cartesian coordinates. A number of improved measurement-conversion techniques have been(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)
– In tracking applications, target dynamics is usually modeled in the Cartesian coordinates, while target measurements are directly available in the original sensor coordinates. Measurement conversion is widely used such that the Kalman filter in the Cartesian coordinates can be applied. A number of improved measurement-conversion techniques have been(More)
—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)
– The most important problem in the application of the multiple-model approach is the design of the model set used. This paper deals with this challenging topic in a general setting, along with model-set choice and comparison. General and representative problems of model-set design, choice, and comparison are considered. Modeling of models as well as true(More)
Most estimators and filters provide assessments of their own estimation errors. Are these self-assessments trustable? What is the degree to which they are trustable? This paper provides practical answers to such questions, referred to as the (level of) credibility of the estimators/filters. It formulates the concept of credibility, proposes several(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)