Performance Analysis of GPS Aided Geo Augmented Navigation (GAGAN) Over Sri Lanka

Abstract

Abstract. Satellite-Based Augmentation Systems (SBAS) are being developed worldwide due to their unique advantage of wide area coverage. GPS Aided Geo Augmented Navigation (GAGAN) is an Indian implementation of SBAS, with three (03) geo stationary satellites in space covering a huge area even beyond Indian Territory. This study focused on analyzing the improvement in position solution with GAGAN corrections over Sri Lanka. In order to test its performances, several dual and single frequency GNSS receivers were used in this experiment, one receiver was configured as SBAS receiver and other two were kept as GPS stand-alone receivers. Observations were carried out over seven (07) known control stations of six (6) different districts to investigate its coverage over Sri Lanka. At each of the tested stations the GAGAN active L1 receiver has always shown a significant accuracy improvement over L1 uncorrected observations. Further, five out of the seven (7) observation locations the calculated average 3D positional errors were lower than 1m. Almost 79% of observations (out of 24 hours of observations) have shown acceptable 3D positional accuracy, of less than 1m, for many spatial data collection applications. However, the local DGPS correction has shown higher reliability than GAGAN corrections with almost 85% of observations with less than 1m, 3D positional error.

Performance improvement of the GAGAN satellite-based augmentation system based on local ionospheric delay estimation in Thailand

“…The LPV-200 and APV-I services (RTCA 2006) have availabilities of about 92% and 70%, respectively. Next, the GPS aided Geo Augmented Navigation (GAGAN) SBAS system over Sri Lanka was assessed by Dammalage et al (2017), and it supports the APV-I service. Although SBAS services do not support the APV-II and CAT-I categories (the precision approach (PA) model based on the ICAO standard), some augmented parameters have been investigated for improvements.…”

Use of regression techniques for rice yield estimation in the North-Western province of Sri Lanka

“…Nevertheless, not many previous studies have been carried out in Sri Lanka to provide information on the effect of climate variability on crops cultivated in mass scale and on the reliable techniques to predict the yield using climate data. Dammalage, De Silva and Satirapod (2017) using a model based on satellite remote sensing data for estimating the pre-harvest rice yield in the Kurunegala district of Sri Lanka from 2007 to 2016, showed that seasonal rice yield could be predicted one month ahead of harvesting with an average overall accuracy of 92%. However, they revealed that the extent of cultivated paddy lands needs to be identified accurately as some of the paddy fields considered in their study had not been cultivated regularly during each season.…”