Kalman filtering with no A-priori information about noise-White noise case: Part I: Identification of covariances

1973 ◽  
Author(s):  
S. Godbole
Keyword(s):  
White Noise ◽  
Kalman Filtering ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information ◽  
Case Part

scholarly journals Unconstrained, robust Kalman filtering for GNSS troposphere tomography

2013 ◽  
Vol 6 (5) ◽  
pp. 9133-9162 ◽  
Author(s):  
W. Rohm ◽  
K. Zhang ◽  
J. Bosy
Keyword(s):  
Water Vapour ◽  
Kalman Filtering ◽  
Weather Prediction ◽  
A Priori ◽  
Real Data ◽  
Reference Station ◽  
A Priori Information ◽  
Dependent Observations ◽  
Integrated Water Vapour ◽  
Priori Information

Abstract. The mesoscale variability of water vapour (WV) in the troposphere is a highly complex phenomenon and modeling and monitoring the WV distribution is a very important but challenging task. Any observation technique that can reliably provide WV distribution is essential for both monitoring and predicting weather. GNSS tomography technique is a powerful tool that builds upon the critical ground-based GNSS infrastructure – Continuous Operating Reference Station (CORS) networks and can be used to sense the amount of WV. Previous research suggests that 3-D WV field from GNSS tomography has an uncertainty of 1 hPa. However all the models used in GNSS tomography heavily rely on a priori information and constraints from non-GNSS measurements. In this study, 3-D GNSS tomography models are investigated based on an unconstrained approach with limited a priori information. A case study is designed and the results show that unconstrained solutions are feasible by using a robust Kalman filtering technique and effective removal of linearly dependent observations and parameters. Discrepancies between reference wet refractivity data derived from the Australian Numerical Weather Prediction (NWP) model (i.e. ACCESS) and the GNSS tomography model using both simulated and real data are 4.2 ppm (mm km−1) and 6.5 ppm (mm km−1), respectively, which are essentially in the same order of accuracy. Therefore the accuracy of the integrated values should not be worse than 0.06 m in terms of zenith wet delay and the integrated water vapour is a fifth of this value which is roughly 10 mm.

scholarly journals Limited constraint, robust Kalman filtering for GNSS troposphere tomography

2014 ◽  
Vol 7 (5) ◽  
pp. 1475-1486 ◽  
Author(s):  
W. Rohm ◽  
K. Zhang ◽  
J. Bosy
Keyword(s):  
Kalman Filtering ◽  
Weather Prediction ◽  
A Priori ◽  
Real Data ◽  
Satellite System ◽  
Reference Station ◽  
A Priori Information ◽  
Dependent Observations ◽  
Priori Information ◽  
Nwp Model

Abstract. The mesoscale variability of water vapour (WV) in the troposphere is a highly complex phenomenon and modelling and monitoring the WV distribution is a very important but challenging task. Any observation technique that can reliably provide WV distribution is essential for both monitoring and predicting weather. The global navigation satellite system (GNSS) tomography technique is a powerful tool that builds upon the critical ground-based GNSS infrastructure (e.g. Continuous Operating Reference Station – CORS – networks) that can be used to sense the amount of WV. Previous research shows that the 3-D WV field from GNSS tomography has an uncertainty of 1 hPa. However, all the models used in GNSS tomography heavily rely on a priori information and constraints from non-GNSS measurements. In this study, 3-D GNSS tomography models are investigated based on a limited constrained approach – i.e. horizontal and vertical correlations between voxels were not introduced, instead various a priori information were added into the system. A case study is designed and the results show that proposed solutions are feasible by using a robust Kalman filtering technique and effective removal of linearly dependent observations and parameters. Discrepancies between reference wet refractivity data derived from the Australian Numerical Weather Prediction (NWP) model (ACCESS) and the GNSS tomography model using both simulated and real data are 4.2 ppm (mm km−1) and 6.2 ppm (mm km−1), respectively, which are essentially in the same order of accuracy.

Sign in / Sign up

Export Citation Format

Share Document