Characterization of Inter Constellation Bias between NavIC and GPS and Methods for Performance Improvement in Positioning

Abstract

The introduction of new navigation satellite constellations has opened up tremendous opportunity for commercial receivers to utilize larger number of satellite measurements for positioning. Generally, in these receivers, inter constellation bias (ICB) is treated as an unknown parameter and is estimated along with computation of solution. The accurate estimation of ICB prior to execution of positioning algorithm can enhance the solution availability of multi constellation receivers in GNSS scarce environment. This is particularly useful for terrestrial receivers which operate in dense forests, urban canyons and high dynamic receivers used in launch vehicle applications. As a special case, the behavior of ICB between GPS and NavIC constellations is studied in this paper. The objective of this work is to generate a reliable and fairly accurate model of the Inter-Constellation Bias (ICB) between GPS and NavIC system. This model can be used to estimate the value of ICB for a short duration and its usage enables the receiver to use a minimum number of satellites (lowest possible number being 4) from GPS and NavIC constellations for accurate positioning in a GNSS scarce environment. It is possible to generate reliable solution with good PDOP using five or six satellites, whereas the conventional method of ICB estimation along with position computation requires one more satellite and may result in relatively poor PDOP under GNSS scarce environment. In this study, ICB between GPS and NavIC constellation is estimated using NavIC and GPS independent navigation solutions computed over Indian region (Thiruvananthapuram, India- Lat: 8.53522°, Long: 76.9929°) for more than a year. The estimation is done using three independent GNSS Receivers: M/s NovAtel make, M/s Accord make and in house developed high dynamic GNSS Receiver. The overall characterization of ICB variation is carried out and further fine refinements are being attempted. Fine resolution data samples of one second and coarse resolution data samples of one day is used for short term and long-term characterization of ICB. The navigation computation is done using predicted ICB from the model. The obtained results are excellent and closely matching with the conventional algorithm which uses ICB as one of the unknown parameters in position computation.