Multiple model adaptive estimator with model set update

Multiple Model Adaptive Estimator (MMAE) which is the simplest robust estimator, relies on a finite number of time-invariant models which approximate the true mode of a system. Filters in MMAE update relative importance of their estimate, when new measurements are available. However, MMAE has got a basic drawback; each filter in MMAE is associated with a time-invariant model of the system. Furthermore, the prototype models are created with few information available about the system, before MMAE is started. In this paper, a method is proposed to update parameters of these models and move them in the domain of interest where they can approximate the true mode of a system better. To this end, an error model is designed and least square estimation is employed to update those models used by MMAE. This robust estimator which is proposed in this paper, was tested on a typical mobile robotic problem, i.e. estimation of the odometry model parameters.

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