TEC and Instrumental Bias Estimation of GAGAN Station Using Kalman Filter and SCORE Algorithm

The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.

The variation of the estimated GPS instrumental bias and its possible connection with ionospheric variability

Based on the analysis of the satellite DCB data estimated by our method and the Center for Orbit Determination in Europe(CODE)from 1999 to 2011,the features of the temporal variation of differential code biases(DCB)are studied.Summarily,there are three types of variations in DCB on different time scales.The first one is the day-to-day variation that exhibits more obviously in solar maximum years.The second one is the variation with about one year periodic variation that behaves more obviously from 1999 to 2004.The last one is the monotonously descending tendency from 1999 to 2010.Considering the basic ionospheric approximation in DCB estimation method,the features of the variability of the ionospheric morphology from 1999to 2010 are also displayed based on the ionospheric characteristic parameters.It can be concluded that the day-to-day and annual variation of the estimated global positioning system(GPS).DCB is related to the ionospheric variability.The variation of DCBs on solar cycle time scale includes the real hardware DCBs and pseudo-DCBs induced by ionospheric variation.No doubt,these kinds of"pseudo"variations of DCB will affect the precision of ionospheric total electron content(TEC)derived from the GPS data.In addition,this study is helpful for evaluating the influence of ionospheric weather on TEC derivation and is also useful for developing one estimation method of DCB with more stability and precision through introducing a more practical ionospheric model.

The influence of ionospheric thin shell height on TEC retrieval from GPS observation

We investigate the influence of assumed height for the thin shell ionosphere model on the Total Electron Content（TEC） derived from a small scale Global Positioning System（GPS） network. TEC and instrumental bias are determined by applying a grid-based algorithm to the data on several geomagnetically quiet days covering a 10 month period in 2006. Comparisons of TEC and instrumental bias are made among assumed heights from 250 km to 700 km with an interval of 10 km. While the TEC variations with time follow the same trend, TEC tends to increase with the height of the thin shell. The difference in TEC between heights 250 km and 700 km can be as large as～8 TECU in both daytime and nighttime. The times at which the TEC reaches its peak or valley do not vary much with the assumed heights. The instrumental biases, especially bias from the satellite, can vary irregularly with assumed height. Several satellites show a large deviation of～3 ns for heights larger than 550 km. The goodness of fit for different assumed heights is also examined. The data can be generally well-fitted for heights from 350 km to 700 km. A large deviation happens at heights lower than 350 km. Using the grid-based algorithm, there is no consensus on assumed height as related to data fitting. A thin shell height in the range 350-500 km can be a reasonable compromise between data fitting and peak height of the ionosphere.