Expected-Mode Augmentation for Multiple-Model Estimation
نویسندگان
چکیده
In this paper a new approach, referred to as expected-mode augmentation (EMA), for multiple-model (MM) estimation is proposed. It is intended to enhance the performance of an MM algorithm in the cases of a continuous mode space. In this approach, the original fixed or variable model set is augmented by a model that intends to match the expected value of the true mode, which is readily computed from the MM estimator as a probabilistically weighted sum of modal states over the model set. This makes it possible to cover at a certain accuracy level a large mode space by a relatively small number of models. Theoretical analysis and justification of the approach is presented. Performance evaluation is conducted by simulation of several EMA-IMM designs for maneuvering target tracking.
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