In normal working conditions it is possible to achieve sufficient attitude estimation accuracy for a satellite using regular Kalman filter algorithm. On the other hand, when there is a fault in the measurements, the Kalman filter fails in providing the required accuracy and may even collapse over time. In this paper, a Robust Kalman filtering method is proposed for the attitude estimation probl...

In 1960 Rudolph E. Kalman published his now famous article describing a recursive solution to the discrete-data linear filtering problem (Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME—Journal of Basic Engineering, 82 (D), 35–45, 1960). Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of e...

Abstract This study investigates the empirical validity of the variability hypothesis in Turkey for the period of February 2005-November 2015, by using cross-sectional relative price data and by focusing on the assumptions of linearity and stability. The linearity assumption between the two variables is ensured by estimating quadratic regression equation. The assumption of stability is secur...

The Kalman Filter is a time series estimation algorithm that is applied extensively in the field of engineering and recently (relative to engineering) in the field of finance and economics. However, presentations of the technique are somewhat intimidating despite the relative ease of generating the algorithm. This paper presents the Kalman Filter in a simplified manner and produces an example o...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...

The influence of the noises uncertainty on the Kalman filter performance is characterized by sensitivity functions. Relationships for computing these functions are derived and used both for synthesizing a Kalman filter with reduced sensitivity (KFRS) and a self-tuning Kalman filter (SKF). The results are illustrated by examples.