Self-Localization Estimation for Mobile Robot Based on Map-Matching Using Downhill Simplex Method

This paper describes a map-matching method which utilizes a downhill simplex method for self-localization estimation of a mobile robot for indoor and outdoor application. Although particle filter is widely established as a method of map-matching, it requires considerable time for recovery when the correct position is unidentifiable. One of the features of the downhill simplex method proposed in this paper is that the search point distribution is wide when it is challenging to determine a point as the correct position. However, it immediately shrinks when the correct position is identified. In this study, it is compared with particle filter and demonstrates the effectiveness of the proposed method through a discussion on the difference between the search methods.

An automatic and effective parameter optimization method for model tuning

Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.