How to Solve GNSS Problem in Critical Environment? P. Brida*, M. Mlynka* and J. Machaj* * University of Zilina/FEE - Department of Telecommunications and Multimedia, Zilina, Slovakia peter.brida@fel.uniza.sk, michal.mlynka@fel.uniza.sk, juraj.machaj@fel.uniza.sk Abstract—Information about user position is very important due to increasing interest in Location Based Services (LBSs). Nowadays, service providers try to provide ubiquitous LBSs. It leads to solve ubiquitous positioning, i.e. localize mobile user anywhere and anytime. This challenge mainly depends on the actual environment where user position is determined. The providers try to divide all environments to two basic types: outdoor and indoor. Positioning in both types requires different ways to estimate position. This paper is focused on outdoor environment. GNSSs (Global Navigation Satellite Systems) provide acceptable reliability in outdoor, but are not very reliable in dense urban areas. Entrances to buildings can also be considered as critical environment, because of interconnection between outdoor and indoor. Therefore user positioning should be as reliable as possible. Generally, tall buildings cause the biggest problems, because mobile device is in GNSS signal shadow and positioning result is negative affected. This paper analyses these problems and try to propose a solution. The proposal is based on alternative positioning solutions based on WLAN, GSM network and Android Location Provider (ALP). These systems are compared to GPS (Global Positioning System) from accuracy point of view. I. INTRODUCTION Location based services (LBSs) attract more and more users every year. The amount of LBSs is expected to significantly grow over next years. Therefore positioning technologies that provide means to localize mobile devices in unknown environments are interesting for research in order to provide more reliable, more accurate and generally better results for users. Modern LBSs are offered not only in outdoor, but also in indoor environment [1]. Many of these services are useful for daily life from the social point of view, e.g. passenger navigation at airports, vehicle navigation or patient monitoring in hospitals [2], [3]. Last mentioned group is very important, because patient is monitored mainly from health point of view. In case of the patient health problems, physician knows his position. The patient can move in both indoor and outdoor environments. Therefore the patient position needs to be determined everywhere. Positioning in both types of environment requires different ways to accurately estimate position. Indoor positioning is mainly based on WLAN (Wireless Local Area Network) – Wi-Fi infrastructure utilization and various methods use sensors mounted on the user. Outdoor positioning seems to be solved by GNSSs (Global Navigation Satellite Systems). These ensure acceptable reliability in outdoor. On the other hand, we have to say that GNSSs are not so ideal. They are not very reliable in dense urban areas where direct view on satellites is often not presented. The most critical environment for these LBSs is entrances to buildings. Entrance to a building is interconnection between outdoor and indoor therefore user positioning should be as reliable as possible. GNSSs are not so reliable near buildings, because user (mobile device) is in the GNSS signal shadow and positioning results are negatively affected. Negative impacts are longer time to first fix if position is determined, positioning accuracy falls down etc. Finally, we can say that reliability is not sufficient in the specific areas for this kind of services. Therefore we analyze this problem and try to find a solution. There are more real possibilities how to replace GNSS with alternative positioning system which will be able to estimate position instead of GNSS in the specific areas. These alternative systems should be more reliable with requested positioning accuracy. We will test positioning systems based on Wi-Fi and GSM network described in [4], [5]. These results will be compared with currently widely used solution implemented in all Android smartphones. We will call it Android Location Provider (ALP) in this paper. This solution determines position based on availability of cell tower and Wi-Fi access points [6]. Performance of GNSS will be compared with three different positioning solutions in real live experiments. The only currently operational GNSS is Global Positioning System (GPS). It will be compared to the other positioning possibilities in terms of accuracy and reliability. The rest of the paper is structured as follows. Section 2 introduces relevant positioning systems. In Section 3, experimental setup is presented. In Section 4, the experimental results are presented and discussed. Section 5 concludes the paper and suggests some future studies. II. TESTED POSITIONING SYSTEMS As noted above GPS and three alternative positioning systems will be tested in this paper. Principle of GPS will be roughly explained. Detailed explanation can be found in [7]. Two tested positioning systems based on Wi-Fi and GSM were designed at the University of Zilina. System based on Wi-Fi is called WifiLOC [5]. System based on GSM network was designed in [4]. Basic principles of WifiLOC are described in the following part. C. Fingerprinting The fingerprinting method relies on a uniqueness of radio fingerprints in a similar way than forensic science does with human fingerprints. The radio fingerprints are vectors of miscellaneous radio signal parameters such as received signal strength, timing advance or angles. These vectors are coupled with position coordinates and altogether form a database of well-known spots – reference points, where these parameters are known. Fingerprinting has two phases, so-called offline and online phase, which are described in following sections. Figure 1. The architecture of the WifiLOC system A. WifiLOC Positioning System WifiLOC is primarily used in indoor, but it can also be implemented in the outdoor environment. WifiLOC utilizes signal information for positioning from surrounding Wi-Fi networks. The system is based on the fingerprinting positioning method and signal strength information. WifiLOC is implemented as a mobile-assisted positioning concept. It means that the necessary measurements are done in a localized mobile station and measured results are forwarded to the network part Localization Server (LCS). The position is estimated (calculated) on a server side. The architecture of the system is depicted in Fig. 1. The system is based on the client-server architecture. The entire architecture could be divided into the three almost independent parts: • localization server, • network of reference stations (access points), • mobile station - user, client. B. GSM Based Positioning System Principle and architecture of the GSM based positioning systems is very similar to WifiLOC. In this case, Base Stations (BSs) forms network of reference stations. Both systems utilize fingerprinting positioning method. It is described in the following section. Offline phase Area where localization services will be offered is divided into small cells. Each cell is represented by one reference point (see Fig. 1). Reference points are represented by geographic coordinates. Information about Received Signal Strength (RSS) values from all reference stations (AP or BS) in range are measured at each reference point. Element of radio map has the form: Pj = ( N j , α ji , β ji ,θ j ), j = 1,2..., m, (1) where Nj is number of j-th reference point, m is the number of all reference points, αji is the vector of RSS values,βji stands for the identifier of APs and parameter θj obtains additional information which can be used during the localization phase. Values βji are tagged by Media Access Control (MAC) address and Cell identity (CID) for Wi-Fi and GSM networks, respectively [8]-[11]. Online phase During the online phase the server uses a deterministic nearest neighbor algorithm to estimate the location of the mobile device. Actual measured RSS values received by the Smartphone are compared with the values Pj stored in the database using the Euclidean distance. Euclidean distance represents the shortest distance between two vectors in Cartesian coordinate system and is defined by: n d Eij = (∑ aik − b jk ) 2 (2) k =1 where n is number of elements in vector, aik represents kth element of vector A and bjk represents k-th element of vector B. Position of the reference point with the smallest Euclidean distance is considered as the estimated position [8]-[11]. Figure 2. Radio map for fingerprinting using RSS D. Android Location Provider Android SDK includes localization library which offers mobile device localization by a network provider function. We called it Android Location Provider (ALP) in this paper. This function determines the location of the mobile device based on availability of cell towers and Wi-Fi APs. Results are retrieved by mean values of a network lookup. This module does not provide high accuracy. On the other hand, this module can provide localization in an unknown urban environment [12], [13]. TABLE I. AVERAGE LOCALIZATION ERROR OF THE PARTICULAR POSITIONING SYSTEMS Positioning systems Localization error [m] Figure 3. Experimental area – University of Zilina campus E. Global Positioning System GPS system was made available to civilians in 1996 for navigation purposes, it is free of charge. An unobstructed line of sight from the receiver to the satellites is necessary to obtain a location. The accuracy of the position estimate depends on the number of used satellites and satellite geometry. It is clear that GPS is not able to localize mobile device in critical areas. The achieved localization error by standard GPS chipset implemented in smartphones can be in the range of 4 m in the open outdoor environment. In the urban environment the accuracy can significantly decrease. III. EXPERIMENTAL SETUP Generally, GPS works reliable in outdoor environment. As was mentioned above, critical areas are places near to buildings because GPS is not able to quickly and precisely fix position. Therefore we decided to analyze this area from noted parameters point of view. As shown in the Fig. 3, area near the buildings was chosen. In this area poor GPS coverage was expected. GPS 26.31 WifiLOC 4.82 GSM 5.32 ALP 69.89 Investigated area is 22x16 meters large. Measurements during the offline phase were performed in a grid, with points spaced 2 m apart. Existing radio infrastructure with three added AP was used. 18 APs and 11 BTSs were detected in total. Measurements were performed using HTC Legend smartphone. This smartphone is equipped with all necessary platforms: GPS, GSM, Wi-Fi and it is able to localize by means of Android Location Provider. Firstly, geo-points in a chosen area were selected. These geo-points were targeted by Trimble VX. Chosen geodetic method guarantees targeting points with localization error of 4 cm. These points are accurate reference points and will be used as reference for four tested positioning systems. Radio maps for positioning systems based on fingerprinting positioning methods need to be created. Process of radio map creation is depicted in Fig. 4. During the offline phase, fingerprints by GSM and Wi-Fi positioning system were created in all targeted points. These fingerprints were sent to the localization server and stored in the radio map database. All preliminary steps are done and evaluation of four positioning system can be performed. 100 random position estimates on the observed area were performed to evaluate the performance of individual localization system. It means that we achieved 100 results for each system, i.e. 400 results. Localization error of particular system was calculated as distance between real - precise position (obtained by Trimble VX) and the estimated position. This distance was obtained by Vincenty formula [14]. Vincenty formula is commonly used in the geodesy to calculate the distance between two geo-points in WGS 84 system. Obtained results were statistically processed and are analyzed in the next section. IV. EXPERIMENTAL RESULTS This section analyses experimental results. Mean values of positioning error of the individual positioning systems are shown in Table 1. In Fig. 5-8, CDF (Cumulative Distribution Function) of positioning error for the individual positioning systems are shown. Empirical CDF 1 0,9 0,8 0,7 CDF 0,6 0,5 0,4 0,3 min: 1.3051 max: 50.4378 mean: 26.3132 median: 29.6795 0,2 0,2 0 Figure 4. Process of radio maps creating 0 5 10 15 20 25 30 35 Localization error [m] 40 45 50 55 Figure 5. Location error CDF of GPS module measured by smartphone HTC Legend 60 Empirical CDF 1 0.9 0.8 0.7 CDF 0.6 0.5 0.4 0.3 min: 0 max: 10.7083 mean: 4.8169 median: 4.0831 0.2 0.1 0 0 1 2 3 4 5 6 7 Localization error [m] 8 9 10 11 12 Figure 6. Location error CDF of WifiLOC Empirical CDF 1 0.9 0.8 0.7 CDF 0.6 0.5 0.4 0.3 min: 0 max: 12.8304 mean: 5.3226 median: 4.4079 0.2 0.1 0 0 1 2 3 4 5 6 7 8 9 Localization error [m] 10 11 12 13 14 Figure 7. Location error CDF of GSM positioning system Empirical CDF 1 0.9 0.8 0.7 CDF 0.6 0.5 0.4 0.3 min: 11.2308 max: 234.0618 mean: 69.8860 median: 19.0924 0.2 0.1 0 0 25 50 75 100 125 150 Localization error [m] 175 200 225 250 Figure 8. Location error CDF of Android Location Provider As shown in Fig. 5, GPS localization error in observed critical area is poor. Median value of the positioning error is app. 30 m and it is not acceptable for LBSs as navigation when mobile device should be navigated to the entrance of building. Maximum error was 50.43 m. Poor performance of the GPS in this environment was assumed, therefore we tested alternative solutions. Promising results were achieved by both WifiLOC and GSM based positioning systems. Results are depicted in Fig. 6 and Fig. 7. WifiLOC achieved slightly better results; median values are app. 4 m and 4.4 m respectively. Maximum achieved errors were 10.71 m and 12.83 m respectively. In these cases, positioning error is approximately 87 % smaller compared to GPS. Positioning results obtained by means of Android Location Provider are shown in Fig. 8. Android Location Provider positioning results are better than GPS, but worse when compared to the best solution WifiLOC. Median value is 19.1 m and maximum error was 234 m. These results seem to be not sufficient for navigation. Exact principle of this positioning solution is not generally known. Therefore we are not able to determine why the poor positioning accuracy was achieved. Optimistic results of Wi-Fi and GSM based solutions were caused by a good necessary infrastructure and a high quality of the radio map. The radio map creation is the main disadvantage of these systems. On the other hand, ALP does not need radio map creation and it is more flexible solution. On the basis of the achieved results we recommend to implement alternative positioning systems in the areas where problems with GPS are assumed. Obviously, some necessary additional steps should be performed, but provided positioning estimates are more reliable. V. CONCLUSION AND FUTURE WORK Main goal of the paper was to determine problems of GNSSs in critical areas. We assume that areas near buildings are critical and crucial to provide reliable LBSs. In light of these assumptions we defined experimental scenario and performed extensive real live measurements. We analyzed four different positioning solutions: standard GPS, WifiLOC, GSM based solution and Android location provider implemented in all smartphones equipped with Android operation system. The achieved results confirmed our assumption that GNSS (e.g. GPS) is not always the best solution in the outdoor environment. Reliability and accuracy of the system was decreased under the acceptable level. On the basis of achieved results, it can be concluded that WifiLOC and GSM based solution offer better accuracy near the buildings compared to GPS and ALP. These systems seem to be reliable alternative solutions against the GPS in the mentioned critical areas. It is very important from seamless positioning point of view. These systems can be built on surrounded infrastructure. The radio map creation is the only disadvantage of these systems. But radio map creation can be implemented by simulation tools without big effort. Combination of all tested solutions can lead to reliable and ubiquitous positioning system. This positioning system can ensure providing of modern LBSs in the both outdoor and indoor environments simultaneously. ACKNOWLEDGMENT This work has been partially supported by the Slovak VEGA grant agency, Project No. 1/0394/13 and by REFERENCES [1] [2] Ondrus, J., Dicova, J. “Navigation system Galileo – support technology for ITS,” Journal of Information, Control and Management Systems, vol. 9, no. 3, pp. 435-442, 2011. O. Krejcar, L. Motalova, “Home care web services evaluation by stress testing,” 1st International Conference on e-Technologies and Networks for Development (ICeND 2011), Dar-es-Salaam, Tanzania, Springer, E-technologies and networks for development, Communications in Computer and Information Science, vol. 171, pp. 238-248, 2011. [3] [4] [5] [6] [7] [8] D. Vybiral, M. Augustynek, M. Penhaker, “Devices for position detection,” Journal of vibroengineering, vol. 13, iss. 3, pp. 531535, 2011. J. Benikovsky, P. Brida, “Positioning system based on fingerprinting method for mobile cellular networks,” The Mediterranean Journal of Computers and Networks, vol. 5, pp. 59-67, iss. 2/2009. P. 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