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This paper studies the problem of simultaneous deciding on hypotheses and estimating a random parameter. We propose a joint decision and estimation (JDE) formulation, which amounts to minimizing a risk related to both decision and estimation while decision performance is also constrained within a tolerable level. The risk used in this paper is a weighted sum of estimation costs conditioned on correct...
How to fuse/combine state estimates that are obtained based on different models (e.g., a CV model, a CA model, and a CT model)? This paper provides a theoretical solution to such problems and beyond. Conventional multiple-model estimation methods use models defined in a common state space. In this paper, we discuss the advantage of using heterogeneous state space for different models in the multiple-model...
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