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This paper deals with the position estimation problem by using the Kalman Filter with compensations for unexpected observations. In the position estimation problem, robot observations sometimes yield unexpected values, resulting in the deterioration of the estimation accuracy. For example, visual observation with an unmanned aerial vehicle often yields unexpected results because of blurred images. In this paper, we propose a method to assigns weights to the observations in order to remove the effects of unexpected observations. In the proposed method, unexpected observations are detected by comparing the observation values with its estimates; the weights of these observations are then determined. On the basis of simulation and experimental results, we demonstrate that a robot's position can be estimated by the proposed method.

This paper addresses wind power prediction, which is known to be a key technology in energy management systems. In this paper, a 24-h-ahead power prediction method using a filter theory is proposed for wind power generation. The prediction method is a simple algorithm. The procedure of prediction consists of two steps: the data processing and the calculation of the predicted values. In data processing, in order to obtain the correlative data from the database, we employ just-in-time modeling. In the calculation of the predicted values, we propose a regression model for wind speed and wind power, and the unknown parameters are estimated using a constrained Kalman filter. Moreover, in the procedure used to estimate the unknown parameters, reduction and convergence of the variables are also guaranteed. Finally, the advantages of the proposed method over the conventional method are shown through actual prediction evaluations. C⃝ 2016 Wiley Periodicals, Inc. Electr Eng Jpn, 198(3): 86-96, 2017; Published online in Wiley Online Library (wileyonlinelibrary.com).

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