KSCE Journal of Civil Engineering
Surveying and Geo-Spatial Information Engineering
Vol. 11, No. 6 / November 2007 pp. 303~310
Ajax GIS Application for GNSS Availability Simulation By Yang-Won Lee*, Yongcheol Suh**, and Ryosuke Shibasaki*** ···································································································································································································································
Abstract This paper describes an Ajax (Asynchronous JavaScript and XML) GIS (geographic information system) application for the simulation of GNSS (Global Navigation Satellite System) availability in dense urban areas. In the forthcoming GNSS environment, satellite visibility will be greatly increased owing to the integration of multiple GNSSs such as the American GPS (Global Positioning System), the Russian GLONASS (Global Navigation Satellite System), the European Union’s GALILEO, and the Japanese QZSS (Quasi-Zenith Satellite System). However, in dense urban areas, the improvements of position accuracy may be limited because obstruction of the signal by high buildings results in bad geometries and multipath effects. To evaluate the spatiotemporally varying availability of GNSS positioning, we built an estimation model that computes the number of visible satellites, the values of DOP (dilution of position), and the amount of multipath errors, according to the location and time of a user. Then, the GNSS availability components were visualized in an Ajax-based Web application that provides a desktop-like interactiveness through the asynchronous data transfer between client and server. This Web simulation shows when and where the navigation services by integrated GNSS are available or appropriate in urban canyons. As a feasibility test, we demonstrated an experimental simulation for the Shinjuku ward of Tokyo filled with skyscrapers. Keywords: GNSS (Global Navigation Satellite System), Web GIS (Geographic Information System), Ajax (Asynchronous JavaScript and XML) ···································································································································································································································
1. Introduction The GNSS (Global Navigation Satellite System) has played a key role in modern navigation services such as car navigation system, personal navigation services, and location-based services. In addition to the American GPS (Global Positioning System) and the Russian GLONASS (Global Navigation Satellite System) that are currently operational, the European Union’s GALILEO and the Japanese QZSS (Quasi-Zenith Satellite System) will be fully operational by the mid-2010s. The integration of multiple GNSSs will elevate the usability of the navigation services (Dellago et al., 2003; Kawano et al., 2004; Seynat et al., 2004; Wu et al., 2004; Feng and Wang, 2007; Januszewski, 2007; Lavrakas, 2007). In general, if the number of satellites increases, a navigation solution can have more chances to select the subsets of satellite constellation that provide better satellite-user geometries. However, in dense urban areas, the improvements of position accuracy may be limited because obstruction of the signal by high buildings results in bad geometries and multipath effects. Multipath occurs when a signal is reflected back to a receiver off surrounding objects. The multipath is especially troublesome because it contaminates the direct signal and degrades the accuracy. When evaluating the GNSS availability in dense urban areas, local terrain variations should be taken into account because the satellite visibility, satellite-user geometry, and multipath effect are highly dependent on surrounding geographic features like
buildings (Li et al., 2006). Thus, recent 3-D GIS (threedimensional geographic information system) approaches have dealt with simulations of GPS availability, using a signal propagation model combined with 3-D terrain data (e.g., Li et al. 2006; Taylor et al., 2007; Suh and Shibasaki, 2007). In addition to these GPS simulations, we may now need a simulation for an integrated GNSS environment (e.g., GPS + GALILEO + QZSS) that will show a future status of satellite positioning. This paper describes an Ajax (Asynchronous JavaScript and XML) GIS application for the simulation of GNSS availability in dense urban areas. A Web-based simulation is helpful for an open access to the GNSS availability information, and besides, an Ajax application can ensure a more interactive user interface in the Web 2.0 environment. We first built an estimation model of GNSS availability, using Keplerian orbital elements, a ray tracing technique, and a DLL (delay-locked loop) correlation function. This estimation model produces simulation data including the number of visible satellites, the values of DOP (dilution of position), and the amount of multipath errors, with respect to a given location and time. Then, the GNSS availability components were visualized in an Ajax application that provides a desktoplike interactiveness through the asynchronous data transfer between client and server. The Ajax GIS application demonstrates an experimental simulation for the Shinjuku ward in Tokyo filled with skyscrapers.
*Researcher, Center for Spatial Information Science, University of Tokyo, Tokyo, Japan **Member, Assistant Professor, Department of Satellite Information Sciences, Pukyong National University, Busan, Korea (Corresponding Author, E-mail:
[email protected]) ***Director and Professor, Center for Spatial Information Science, University of Tokyo, Tokyo, Japan Vol. 11, No. 6 / November 2007
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2. Gnss Availability Estimation 2.1 Satellite Orbital Model Obtaining the precise position of a satellite is critical to determining a user position in navigation solutions (Kaplan et al., 2006). A satellite position can be defined using the Keplerian orbital elements such as semimajor axis, eccentricity, inclination, right ascension of ascending node (RAAN), argument of perigee, and true anomaly (Fig. 1). The orbital parameters of GPS can be acquired from the Web site of the United States Department of Homeland Security (http://www.navcen.uscg.gov/archives/gps). We used the real orbital parameters for GPS whereas we referred to the parameters in the existing simulation scenarios (Zimmermann et al., 2005; Kogure et al., 2007) for GALILEO and QZSS (Table 1, Table 2). We first derived the geocentric perifocal coordinates of a satellite, using the true anomaly and the radius of the satellite orbit. The true anomaly is calculated using the eccentricity and the mean anomaly. The radius r is defined as a(1-e)/(1+ecosQ),
Fig. 1. Keplerian Orbital Elements (wikipedia.org)
Table 1. Orbital Parameters for GALILEO Satellites in a Simulation Senario No.
Semimajor axis (km)
Eccentricity
Inclination
RAAN (o)
Argument of perigee (o)
Mean anomaly (o)
1
29600.318
0
56
0
0
0
2
29600.318
0
56
0
0
40
3
29600.318
0
56
0
0
80
4
29600.318
0
56
0
0
120
5
29600.318
0
56
0
0
160
6
29600.318
0
56
0
0
200
7
29600.318
0
56
0
0
240
8
29600.318
0
56
0
0
280
9
29600.318
0
56
0
0
320
10
29600.318
0
56
120
0
13.33
11
29600.318
0
56
120
0
53.33
12
29600.318
0
56
120
0
93.33
13
29600.318
0
56
120
0
133.33
14
29600.318
0
56
120
0
173.33
15
29600.318
0
56
120
0
213.33
16
29600.318
0
56
120
0
253.33
17
29600.318
0
56
120
0
293.33
18
29600.318
0
56
120
0
333.33
19
29600.318
0
56
240
0
26.66
20
29600.318
0
56
240
0
66.66
21
29600.318
0
56
240
0
106.66
22
29600.318
0
56
240
0
146.66
23
29600.318
0
56
240
0
186.66
24
29600.318
0
56
240
0
226.66
25
29600.318
0
56
240
0
266.66
26
29600.318
0
56
240
0
306.66
27
29600.318
0
56
240
0
346.66
Source: Zimmermann et al. (2005)
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Ajax GIS Application for GNSS Availability Simulation Table 2. Orbital Parameters for QZSS Satellites in a Simulation Scenario No.
Semimajor axis (km)
Eccentricity
Inclination
RAAN (o)
Argument of perigee (o)
Mean anomaly (o)
1
42164.17
0.099
45
0
270
239.8
2
42164.17
0.099
45
120
270
119.8
3
42164.17
0.099
45
240
270
359.8
Source: Kogure et al. (2007)
where a is the semimajor axis, e is the eccentricity, and Q is the true anomaly. Thus, the coordinates of the satellite can be expressed as x0=rcosQ, y0=rsinQ, and z0=0. Then, we derived the geocentric equatorial coordinates of the satellite from the geocentric perifocal coordinates, using the following 3 u 3 matrix R: § cos: cosZ – sin: sinZ cosi –cos : cosZ – sin: sinZ cosi sin: sini · t t t t t t t t t ¨ ¸ ¨ sin:tcosZt + cos:t sinZtcosi –sin :t sinZt + cos:t Ztcosi –cos:tsini ¸ ¨ ¸ sinZt sini cosZt sini cosi ¹ ©
, where :t is the RAAN at time t, Zt is the argument of perigee at time t, and i is the inclination. Thus, the geocentric equatorial coordinates of the satellite can be expressed as x1 = x0R[1,1] + y0R[1,2] + z0R[1,3], y1 = x0R[2,1] + y0R[2,2] + z0R[2,3], and z1 =x0R[3,1] + y0R[3,2] + z0R[3,3]. Using the position of a satellite as well as the position of a user, we can calculate the elevation of the satellite and the distance between the satellite and the user. In our simulation, a user position is represented in a geographic coordinate system on a map, and the satellite position is also expressed as the same geographic coordinate system. Hence, the geocentric equatorial coordinates of a satellite were converted to the ECEF (EarthCentered Earth-Fixed) coordinates, and then to the JGD2000 (Japan Geodetic Datum 2000) coordinates that were used in the 3-D maps of the estimation model.
2.2 Three-dimensional Terrain Data We used a DiaMap DXF (Drawing Exchange Format) for 3-D terrain data (Fig. 2). The DiaMap DXF is generated from highquality data sources such as 1-m resolution IKONOS satellite imagery and 30-cm resolution DEM (digital elevation model). Our estimation model creates every building surface from the 3D maps, for a more realistic simulation in dense urban areas. 2.3 Satellite Visibility Once the position of a satellite is obtained, the satellite visibility can be determined by the LOS (line-of-sight) between a satellite and a user. We considered a satellite to be visible if the satellite-user segment does not intersect with any building surface in 3-D maps. From the visible satellites, we filtered out those with the elevation angle lower than 15o, like ordinary GPS receivers. Even though a low elevation satellite is visible, it is possibly prone to signal contamination. Hence, the number of visible satellites in this simulation refers to that of relatively high elevation satellites. 2.4 Dilution of Precision The DOP is typically used as a measure of the geometric arrangement of satellites. When visible satellites are adequately far apart in the sky, the geometry is regarded as favorable, and the DOP value is calculated as low; when close together, the geometry is unfavorable, and the DOP value is high. Thus, a low DOP is associated with better accuracy due to the wider angular separation among satellites. Suppose, for example, n(nt4)
Fig. 2. A 3-D Model Around the Tokyo Metropolitan Government Building Vol. 11, No. 6 / November 2007
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Yang-Won Lee, Yongcheol Suh, and Ryosuke Shibasaki Table 3. Rating of DOP Values DOP value
1
2-3
4-6
7-8
9-20
21-50
Rating
Idea
Excellent
Good
Moderate
Fair
Poor
satellites are visible to a receiver location. Then, the possible number of satellite combinations will be nC4+nC5+...+nCn, where C is the combination operator. Since the satellite combination and the DOP have a one-to-one relationship, n visible satellites n produce ¦ nCi satellite combinations and the same number of i=4 DOP values. For the sake of convenience in the DOP estimation, n nCi values, with we simply calculated the average of the i¦ =4 respect to HDOP (horizontal DOP), VDOP (vertical DOP), and PDOP (position DOP), respectively. The criteria for DOP evaluation is as Table 3. 2.5 Pseudorange Multipath Error Any satellite-to-user range determination includes an inevitable clock error because the receiver clock is not perfectly synchronized with the satellite clock. Hence, the satellite-to-user range is called a pseudorange. A pseudorange can have additional error sources such as atmospheric delay, multipath delay, and receiver noise. A pseudorange error, the error included in a pseudorange value, corresponds to the difference between a pseudorange measured at a given location and the pseudorange expected for that location (Ward et al., 2006). In dense urban areas, position accuracy can be affected more by multipath than other error sources. Thus, our estimation model focuses on the pseudorange multipath error when dealing with the pseudorange error. In order to model the multipath of GNSS signals in various local terrains, we used a ray tracing technique combined with 3D maps. As illustrated in Fig. 3, every signal was assumed to be
specularly reflected by a mirrorlike surface, which reflects a signal from a single incoming direction to a single outgoing direction. In general, buildings with concrete surfaces can be treated as a specular reflector (Li et al., 2006; Suh and Shibasaki, 2007). If the geometric relationships among a satellite, a building surface, and a receiver are in accordance with specular reflection, the satellite is identified as a multipath satellite for the receiver. Our estimation model emulates the DLL correlation function to derive a pseudorange multipath error for the multipath satellites identified by the ray tracing technique. Since a receiver tries to correlate with both the multipath and the direct signal, the resulting composite signal is distorted in the correlation function (Van Nee et al., 1994; Townsend et al., 2000; Ward et al., 2006). When the delay of a signal is greater than a given threshold of correlator spacing, the signal is regarded as a severe multipath and rejected in advance by a decorrelation process. For the signals with a multipath delay smaller than the given threshold, a correlation peak is tracked by the correlation function. Fig. 4(a) shows an ideal correlation peak without multipath, and Figs. 4(b) and 4(c) present a distorted correlation peak by multipath. Such distortion does not allow a tracking point to be aligned to the desired correlation peak and leads to a pseudorange error in the correlation function. The pseudorange error is positive if a multipath interference is constructive (Fig. 4(b)) whereas a destructive interference causes a negative pseudorange error (Fig. 4(c)). The amount of pseudorange multipath error is calculated using the difference between the ideal correlation peak and the midpoint of the correlator spacing of the composite signal.
3. Ajax Gis Application
Fig. 3. Specular Reflection in Multipath Model
3.1 Ajax Techniques We employed Ajax techniques in simulating the GNSS availability within a rich-client Web application. Using the XMLHttpRequest object in JavaScript, a Web application can trade data with a Web server through the partial renewal of a Web page instead of reloading the whole page. The start-stopstart-stop nature of the Web is eliminated by the Ajax engine that handles the asynchronous interaction between a browser and a server (Garrett, 2005). In the traditional Web application model, a user request cannot be issued before the previous request is responded. The user interface is locked up during the waiting time. On the other hand, in the Ajax model, additional requests can be issued without a lock-up, because a server’s response is asynchronously processed in the background (Fig. 5). Ajax applications take advantage of DHTML (dynamic HTML), which consists of HTML, CSS (cascading style sheets), JavaScript, and DOM (document object model). The following describes the functionalities of these components (Paulson, 2005). - HTML: Web pages become more dynamic and interactive than those using previous HTML versions. 306
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Ajax GIS Application for GNSS Availability Simulation
Fig. 4. Computation Principle of Pseudorange Multipath Error
to the previous call. - DOM: For the dynamic appearance of DHTML pages, the DOM defines the attributes of program objects and the ways in which users interact with the objects. 3.2 GIS Application Development Using the Ajax techniques, we developed a GIS application for the GNSS availability simulation in dense urban areas. Simulation data including the number of visible satellites, the average DOPs, and pseudorange multipath error was recorded in an XML file and imported into the Ajax GIS application. The application is combined with Google Maps and animated according to the location and time of a user. As illustrated in Figure 6, the visualization components include a tracking map, a route map, a DOP viewer, and a monitor for pseudorange multipath error. The tracking map (a) automatically moves along a simulated route while pinpointing a current location. An overall route with the current location is drawn in the route map (b). A spline chart for the average of HDOP, VDOP, and PDOP is animated on the DOP viewer (c). The monitor for pseudorange multipath error (d) flickers like a traffic signal, showing the degree of multipath risk for the 30 GPS, 27 GALILEO, and 3 QZSS satellites.
4. Simulation Experiment Fig. 5. Comparison between Traditional and Ajax Models (Garrett, 2005)
- CSS: Developers and users obtain more control over how a Web page is styled and displayed. - JavaScript: The asynchronous JavaScript allows an application to make a server call without waiting a server’s response Vol. 11, No. 6 / November 2007
For a feasibility test of the Ajax GIS application, we generated the simulation data for an area around the Tokyo Metropolitan Government Building. In dense urban areas, a small change of location may bring about differences in the status of GNSS availability. Thus, we sampled sparsely distributed points along the road and constructed a tracking route using the points. A movement from a point to the next one corresponded to 1 second
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Yang-Won Lee, Yongcheol Suh, and Ryosuke Shibasaki
Fig. 6. Ajax-Based Visualization Components for GNSS Availability Simulation
in this simulation. Fig. 7 shows the screenshots of the tracking map that moves along the route with an automatic pinpointing and panning. The starting time of the simulation was set to 09:00 on December 10, 2006. At intervals of 1 second from that time, the GNSS availability information along the route was recorded in an XML file (Fig. 8). We represented user locations in WGS84
(World Geodetic System 1984) coordinates for the compatibility with Google Maps. Invisible satellites were denoted as “X” in the tag. The multipath status of each satellite was visualized in three colors (green, yellow, and red) depending on the amount of pseudorange multipath error. For the satellite with a pseudorange multipath error exceeding ±10 m, we used red to alert the signal
Fig. 7. Tracking Maps with an Automatic Pinpointing and Panning 308
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Ajax GIS Application for GNSS Availability Simulation
Fig. 8. GNSS Availability Information in XML Format
Fig. 9. GNSS Availability Simulation for the Shinjuku Ward of Tokyo Vol. 11, No. 6 / November 2007
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contamination. The satellites without a pseudorange multipath error were represented in green, and the satellites with the error between 0 and ±10 m were shown in yellow. The numbers of visible satellites for these three types were also displayed in the monitor for pseudorange multipath error. Fig. 9(a) and 9(b) demonstrates the examples of the simulation in the study area.
5. Concluding Remarks In this paper, we discussed an Ajax GIS application for the simulation of GNSS availability. The integration of multiple GNSSs will provide a greatly increased satellite visibility, but the improvements of position accuracy may be limited because obstruction of the signal by high buildings results in bad geometries and multipath effects. To evaluate the availability of GNSS positioning, we built an estimation model that computes the number of visible satellites, the values of DOP, and the amount of multipath errors, according to the location and time of a user. The simulation data from our estimation model was imported into an Ajax GIS application, which automatically navigates along a simulated route while demonstrating the spatiotemporal variations in GNSS availability. This Web simulation shows when and where the navigation services by integrated GNSS are available or appropriate in urban canyons. We confirmed that a GIS simulation was able to extend to a more interactive Web application, by use of the asynchronous data transfer of Ajax. A real-time Ajax GIS application using a wireless communication with GPS clients will be a future work.
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Artech House, Norwood, MA, pp. 21-65. Kawano, I., Mokuno, M., Kogure, S., and M. Kishimoto. (2004). “Japanese experimental GPS augmentation using Quasi-Zenith Satellite System.” Proceedings of Institute of Navigation Global Navigation Satellite System (ION GNSS) 2004, pp. 175-181. Kogure, S., Kishimoto, M., and Sawabe, M., (2007). “Future expansion from QZSS to regional satellite navigation system.” Proceedings of Institute of Navigation National Technical Meeting (ION NTM) 2007, pp. 455-460. Lavrakas, J.W. (2007). “A glimpse into the future: a look at GNSS in the year 2017.” Proceedings of Institute of Navigation National Technical Meeting (ION NTM) 2007, pp. 210-217. Li, J., Taylor, G., Kidner, D., and Ware, M. (2006). “Prediction of GPS multipath effect using LiDAR digital surface models and building footprints.” Lecture Notes in Computer Science, Vol. 4295, pp. 4253. Paulson, L.D. (2005). “Building rich Web applications with Ajax.” IEEE Computer, Vol. 38, No. 10, pp. 14-17. Seynat, C., Kealy, A., and Zhang, K. (2004). “A performance analysis of future Global Navigation Satellite Systems.” Journal of Global Positioning Systems, Vol. 3, No. 1-2, pp. 232-241. Suh, Y. and Shibasaki, R. (2007). “Evaluation of satellite-based navigation services in complex urban environments using a threedimensional GIS.” The Institute of Electronics, Information and Communication Engineers (IEICE) Transactions on Communications, Vol. E90-B(7), pp. 1816-1825. Taylor, G., Li, J., Kidner, D., Brunsdon, C., and Ware, M. (2007). “Modelling and prediction of GPS availability with digital photogrammetry and LiDAR.” International Journal of Geographic Information Science, Vol. 21, No. 1, pp. 1-20. Townsend, B., Wiebe, J., and Jakab, A. (2000). “Results and analysis of using the MEDLL receiver as a multipath meter.” Proceedings of Institute of Navigation National Technical Meeting (ION NTM) 2000, pp. 73-79. Van Nee, R.D.J., Siereveld, J., Fenton, P.C., and Townsend, B.R. (1994). “The multipath estimating delay lock loop: approaching theoretical accuracy limits.” Proceedings of IEEE Position Location and Navigation Symposium (PLANS) ’94, pp. 246-251. Ward, P.W., Betz, J.W., and Hegarty, C.J. (2006). “Interface, multipath, and scintillation.” In: Kaplan, E.D. and Hegarty, C.J. (Ed.) Understanding GPS: Principle and Applications. Artech House, Norwood, MA, pp. 243-299. Wu, F., Kubo, N., and Yasuda, A. (2004). “Performance analysis of GPS augmentation using Japanese Quasi-Zenith Satellite System.” Earth, Planets and Space, Vol. 56, No. 1, pp. 25-37. Zimmermann, F., Haak, T., Steindl, E., Vardarajulu, S., Kalden, O., Hill, C. (2005). “Generating Galileo raw data - approach and application.” Proceedings of Data Systems in Aerospace (DASIA) 2005, CD-ROM.
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(Recived August 13, 2007/Accepted October 22, 2007)
KSCE Journal of Civil Engineering
