Rapid interference suppression using a Kalman filter technique

1983 ◽  
Vol 130 (1) ◽  
pp. 36
Author(s):  
K. Krücker
Keyword(s):  
Kalman Filter ◽  
Interference Suppression ◽  
Filter Technique

scholarly journals Experience Rating of ARIMA Processes by the Kalman Filter

1986 ◽  
Vol 16 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Jukka Rantala
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
State Space ◽  
Theoretical Approach ◽  
Space Form ◽  
The State ◽  
Experience Rating ◽  
Filter Technique ◽  
Main Emphasis ◽  
Arima Processes

AbstractThis paper deals with experience rating of claims processes of ARIMA structures. By experience rating we mean that future premiums should be only a function of past values of the claims process. The main emphasis is on demonstrating the usefulness of the control-theoretical approach in the search for optimal rating rules. Optimality is here defined to mean as smooth a flow of premiums as possible when the variation in the accumulated profit is restricted to a certain amount. First it is shown how the underlying model in its simplest form can be transformed into the state-space form. Then the Kalman filter technique is used to find the optimal rules. Also a time delay in information is taken into account. The optimal rules are illustrated by examples.

scholarly journals Impact of surface data assimilation using an ensemble Kalman filter technique on the improvement of weather prediction

2019 ◽  
Vol 1127 ◽  
pp. 012041
Author(s):  
N J Trilaksono ◽  
M Taqiyya ◽  
N Dewani ◽  
I D G A Junnaedhi ◽  
E Riawan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Filter Technique ◽  
Surface Data

scholarly journals Combining quantitative and qualitative data in business cycle research

1986 ◽  
Vol 17 (3) ◽  
pp. 143-148
Author(s):  
E. Smit
Keyword(s):  
Kalman Filter ◽  
Business Cycle ◽  
South African ◽  
Quantitative Data ◽  
Qualitative Data ◽  
The South ◽  
Filter Technique ◽  
Leading Indicator ◽  
Traditional Approaches

In this article a leading indicator of the South African business cycle is proposed which combines the traditional quantitative data inputs with qualitative data. The integration is achieved via the Kalman filter technique. It is shown that this model surpasses the traditional approaches in accuracy.

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