# Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration  
[PERSON]  
1 School of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, China; [EMAIL_ADDRESS] (Z.G. ); [EMAIL_ADDRESS] (H.Y.) [PERSON]  
1 School of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, China; [EMAIL_ADDRESS] (Z.  
applications [3; 4; 5; 6]. This phenomenon is mainly due to the two advantages of PPP. First, PPP has the capability to efficaciously provide centimeter-level or even millimeter-level absolute positioning solutions for static applications under open-sky environments, such as earthquake monitoring [3; 4], crust movement measuring [5], and water vapor prediction [6]. Moreover, PPP does not need base stations, which makes it possible to obtain high-accuracy PPP solutions at any place as long as there are enough available GPS observations. This is because PPP uses precise error correction models and precise satellite orbit/clock products to mitigate observing errors and estimates the incomplete modeled errors as parameters [1; 7]. Such data processing methods can drastically overcome the challenge that the positioning error may become large along with the increasing base-line, which is an issue inherent to real-time kinematic (RTK) systems [8]. However, compared to PPP, RTK has been more widely applied in the precise positioning and navigation engineering practices, especially in dynamic applications, such as aerial photogrammetry and mobile mapping. The main reason for this fact is that the frequently decreased visible satellite number will lead to insufficient observations for PPP parameter estimation, and lower positioning accuracy and continuity [9; 10]. In order to abate such shortcoming in PPP, the multi-constellation global navigation satellite systems (GNSS) data are utilized [11; 12; 13; 14]. For example, in [11] and [14], data from the BeiDou satellite navigation system (BDS), GLObal NAvigation Satellite System (GLONASS), and Galileo are adopted to improve the GPS PPP solutions. In contrast to the other three GNSS systems (i.e., GPS, GLONASS, and Galileo adopt the medium Earth orbit (MEO) satellites constellation), BDS consists of three types of orbit-satellites, namely geostationary Earth orbit satellites (GEO), inclined geosynchronous satellite orbit satellites (IGSO), and MEO satellites [15]. Theoretically, the existence of GEO and IGSO can provide better satellite tracking and observing geometry structure for users [16], especially in China, because of their special locations. According to [17; 18], BDS is China's global navigation satellite system, which was built around 2000 to provide triple-frequency (BII: 1561.098 MHz, B2I: 1207.140 MHz, and B3I: 1268.520 MHz) civil signals to satisfy the location service requirements of China. Before BDS-3's open service online (i.e., before 27 December 2018), BDS only provided navigation and precise positioning service in the Asia-Pacific region with the operating satellite constellation of five GEOs, five IGSOs, and four MEOs, respectively [19]. Figure 1 shows the sky plot of the current GPS (Figure 1a) and BDS regional system (Figure 1b), which brings significant enhancement to increasing the total visible satellite number around China. As proven by the previous works, the use of BDS observations can increase visible satellite number [11; 14; 15] and upgrade GPS PPP performance significantly in terms of convergence speed, positioning accuracy, and continuity [14]. Figure 1: Sky-plots of GPS satellites (MEO) (**a**) and BDS GEO/IGSO/MEO regional satellites (**b**). MEO is medium Earth orbit, BDS is BeiDou satellite navigation system, GEO is geostationary Earth orbit, and IGSO is inclined geosynchronous satellite orbit. In dynamic applications, users have to frequently pass through avenues, flyovers, and tunnels, especially in urban environments. Under those conditions, satellite signals would be blocked.