Urban Positioning on a Smartphone Real-time Shadow Matching Using GNSS and 3D City Models When life gives you lemons, make lemonade. This article presents a smartphone-based system that uses the GNSS signal “shadows” cast by buildings — usually the satellite- signal–blocking bane of navigating in narrow urban canyons — to improve the accuracy of street- level positioning. The authors present the results of field tests that reveal dramatic reduction of positioning errors and improved availability. LEI WANG, PAUL D GROVES, AND MAREK K ZIEBART UNIVERSITY COLLEGE LONDON 44 InsideGNSS NOVEMBER/DECEMBEER 2013 www.insidegnss.com he poor performance of GNSS and barometric altimeter. A selection and non-line-of-sight (NLOS) signals. user equipment in urban can- of these sensors can help mobile phone Because of surrounding structures, yons in terms of both accuracy users to recognize motion states; con- signals from GNSS satellites may be T and solution availability is a sequently, context-adaptive algorithms receivable in some parts of a street, but well-known problem that arises where can be applied to improve the position- not others. there are tall buildings or narrow streets. ing performance. In combination with a 3D city model, This situation is worse in the cross-street Although such approaches improve a GNSS receiver can determine its posi- direction than in the along-street direc- the availability and robustness of a posi- tion to within one of two areas of the tion. (See Figure 1.) tion solution, they do not particularly street as illustrated in Figure 2. Incorpo- The accuracy in the cross-street improve the cross-street accuracy. rating other GNSS satellite signals allows direction can be of great importance to In recent years, 3D building models users to further refine the position solu- daily life. Numerous street-level appli- have become more accurate and widely tion, matching the observed signal shad- cations require sufficient positioning available and are being treated as a new ows with the predicted shadowing avail- accuracy to perform their functions: for data source for urban navigation that able from the 3D model. example, detecting the lane in which a can improve positioning performance in In previous work, we evaluated and vehicle is operating for lane guidance urban canyons. Some investigators are verified the performance of GNSS in systems and intelligent transportation applying 3D city models to enhance map urban canyons using 3D city models. systems (ITS), location-based advertis- matching and image matching in land Then, we developed a preliminary shad- ing (LBA), augmented-reality applica- vehicle navigation. One line of research ow-matching algorithm that demonstrat- tions, and step-by-step guidance for the employs these models to evaluate GNSS ed the ability to distinguish the pavement visually impaired or tourists. positioning performance with 3D ray from a vehicle lane, and identify the cor- A variety of navigation sensors have tracing or ray intersection techniques. rect side of the street using real-world been used to enhance or augment GNSS This approach demonstrates the practi- GPS and GLONASS measurements to improve navigation performance cal potential of shadow matching and is Next, we proposed a new scheme in highly built-up areas. Road vehicles the focus of this article. that takes into account the effects of typically combine GNSS with odom- satellite signal diffraction and reflec- eters and map-matching algorithms, GNSS Shadow Matching tion by weighting the scores based on while pedestrian navigation users may The principle of shadow matching diffraction modelling and the signal- combine GNSS with cell phone signals, employs the “shadows” of GNSS satel- to-noise ratio (SNR). This was shown Wi-Fi, and/or dead reckoning using lite signals to refine the position solu- to improve the navigation solution of inertial sensors, magnetic compass, tion obtained from line-of-sight (LOS) geodetic GPS and GLONASS receivers. FIGURE 2 The shadow-matching concept: using direct signal reception to localize position (from L. Wang et alia, 2013a, based on P. Groves, 2011). FIGURE 1 Satellite signals with lines of sight (LOS) going across the street are much more likely to be blocked by buildings than signals with LOS going along the street. (from L. Wang et alia, 2013a) www.insidegnss.com NOVEMBER/DECEMBER 2013 InsideGNSS 45 URBAN POSITIONING tion, and stronger non-line of sight (NLOS) reception due to the low gain and linear polarization of consumer-grade GNSS antennas. These differences can degrade shadow-matching performance. Regarding our second motivation for the current focus, smartphones and personal navigation devices (PND) normally have much less computational capability in comparison with a desktop computer or laptop, whereas the existing algorithm has only been run on personal computers. Its computational efficiency has not previously been tested. Thus, optimization of the shadow-matching technique to make it suitable for smart- phone-grade devices is worth investigation. FIGURE 3 The overall system architecture design Consequently, this article proposes and implements a smartphone-based system, aiming at meters-level cross-street accuracy in urban canyons. It will also assess the real-time positioning performance of shadow matching on a smartphone. The following section summarizes the design of the real-time shadow-matching system and presents its optimizations in pre- processing to determine the elevation and azimuth boundary of the buildings in a test area. We then describe the details of appli- cation development on the phone’s Android operating system. Next comes an assessment of real-time experiments apply- ing various criteria to compare the performance of the conven- tional GNSS navigation solution with the shadow-matching system solution. Finally we address the feasibility of larger scale implementation and end with conclusions drawn from the results of this investigation. System Design & Optimization for Real-Time Apps Let’s start by explaining key design choices of the smartphone- based shadow-matching system, including overall architecture, the modified shadow-matching algorithm, and improvements essential to real-time efficiency. FIGURE 4 A workflow of the improved shadow-matching algorithm System Architecture Design. In a full implementation of a shadow-matching system, a server interacts with the smart- Recently, we collected GNSS data collected on a smartphone phone user. The smartphone first sends a positioning request and processed the data using a shadow-matching algorithm with an initial GNSS position to the server. The server then on a PC; however, we still need to conduct a comprehensive gathers the building boundary data that assists in shadow- analysis of these latest efforts to address the issue of computa- matching based on the user’s initial position solution and sends tion efficiency. The series of articles by L. Wang et alia and P. them back to the user. Finally, the smartphone performs the Groves et alia, cited in the Additional Resources section near shadow-matching algorithm and acquires a revised position- the end of this article, describe these initial shadow-matching ing solution. The overall architecture of the shadow-matching investigations. system is illustrated in Figure 3. This article presents a significant development of shadow In terms of algorithm design, shadow matching has two matching — a smartphone-based shadow-matching system phases — the offline phase (the preparation step) and the online that works in real time. This work is motivated by two con- phase (the real-time positioning) — which are executed in four cerns: accommodating the low-grade antenna and the low- steps, represented by the red boxes in Figure 4. computational power typical of smartphones. The off-line phase is conducted to generate a grid of building As for the first concern, most potential applications of boundaries. At the beginning of the online phase, the search shadow matching use consumer-grade GNSS user equipment, area is defined for the shadow-matching position solution, whereas previous shadow matching algorithms were mainly using the initial GNSS position. tested using geodetic GNSS receivers. Moreover, consumer- In the second step, satellite visibility at each grid position is grade GNSS receivers normally cost much less and can be predicted using the building boundaries generated from the 3D subject to worse signal reception, more severe multipath recep- city model. After that, the difference between prediction and 46 InsideGNSS NOVEMBER/DECEMBEER 2013 www.insidegnss.com ment (See the article by P. Groves et alia, the elevation of the building boundary 2013b). In that case, the shadow-match- at the same azimuth. We developed a ing algorithm would only be implement- software toolkit in C++ for generating ed in urban contexts. the grid of building boundaries from a Let’s look at each of the shadow- 3D city model. matching steps in more detail. Step 1: Determine the Search Area Step 0: Generate a Grid of Building for Candidate Positions from the Building Boundaries (Off-Line Phase). In the off- Boundaries on a Grid. The first step of the line phase, building boundaries are shadow-matching algorithm requires an FIGURE 5 An example of a building boundary as generated for each point on a grid of initial GNSS position. In an urban envi- azimuth-elevation pairs in a sky plot. (The outdoor locations. The boundaries are ronment, GNSS accuracy is often poor.. center of the plot corresponds to a 90º eleva- tion or normal incidence) presented from a GNSS user’s perspec- Therefore, it is important to minimize the tive, with the building edges determined effects of NLOS reception and multipath for each azimuth (from 0 to 360 degrees) interference on the position solution. observation is evaluated using a scoring as a series of elevation angles. The results Other available positioning methods scheme, providing a score for each grid from this step show where the building (e.g., Wi-Fi or cell network solutions) point in the search area.