Mass market GPS applications have mainly focused on car navigation over the last five years and contributed to the wide-spread use of GPS receivers. A wide range of applications can be foreseen for pedestrians as well: multimodal navigation, local search, and social networking are a few. But the roll out of most accuracy-critical applications like e-tourism has been slowed by the difficulties of GPS-based positioning for pedestrians in urban areas.

Multipath and masking effects of urban canyons degrade the accuracy of GPS ranging and increase geometric dilution of precision in receivers that operate in dense urban areas. In the case of GPS applications designed for vehicles, the effects of these phenomena on accuracy can be reduced, thanks to the velocity of the user that contributes in averaging multipath and thanks to the use of map matching. But pedestrians do not benefit from the same circumstances, and GPS-based positioning for pedestrians in dense urban areas suffers from inadequate accuracy and integrity. Tests performed in downtown urban areas over a variety of mass market terminals with integrated GPS receivers show 95 percent circular error probable (CEP) performances between 50 and 100 meters.

This article presents a novel approach: a GIS database that contains the geometrical description of buildings is used in the location computation process. Raw data are extracted from the GPS receiver and used in combination with the description of surrounding buildings. The method involves restricting the area of possible locations, mitigating multipath effects, matching GPS measurements with the environment, and using motion models to compute accurate fixes. Unlike usual map matching, the solution maintains the intrinsic freedom of the pedestrian, keeping track of the actual trajectory across squares, courtyards, and gardens.

We achieved an implementation of the method using available GIS (geographic information system) products, enabling the concept to be used as light middleware on today's mass market terminals such as personal navigation devices, smartphones, or personal digital assistants (PDAs), with very little CPU usage. Our tests in several cities showed significant improvements in the accuracy of GPS-based positioning for pedestrians, especially in narrow streets.

Dense Urban Environments

The well-known urban canyon effect on GPS positioning does not equally affect car drivers and pedestrians. At higher speeds, acquisition algorithms, tracking loops, and Kalman filters all better manage to reject or smooth effects resulting from multipath. The different behaviors of pedestrians and cars also contribute to different accuracy results, even in the same places. For instance, pedestrians use sidewalks close to buildings: this reduces satellite visibility, resulting in horizontal dilution of precision (HDOP) degradation.

FIGURE 1 Pedestrian vs. in-car cross-track error in urban area

Tests performed in the center of Toulouse, France, illustrate the discrepancy in accuracy performances between receivers operated by pedestrians and receivers operated by car drivers. Tests were run in an area where streets are approximately 5 to 20 meters wide and 15 to 20 meters high; smartphones with built-in GPS receivers were used. The car was driving at speeds between 30 and 50 km.h-1. More than 30,000 position fixes were collected in car mode and more than 15,000 in pedestrian mode. FIGURE1 plots the cumulative distribution function of cross-track errors for pedestrians and cars, showing clear accuracy degradation in the pedestrian case. For instance, the 95 percent cross-track accuracy was around 30 meters in the pedestrian case, compared to 8 meters in the vehicle case. For lower probabilities, the pedestrian cross-track error is approximately three times as much as in the vehicle case.

FIGURE 2 Typical accuracy discrepancy between in-car and pedestrian users of GPS receivers in dense urban areas

Typical track errors are shown in FIGURE 2. In this figure, the GPS receiver embedded in the vehicle computes locations that perfectly match the street, whereas fixes computed by the pedestrian receiver swings back and forth across the street. Such positioning errors have prevented the emergence of applications intended for pedestrians, starting with pedestrian navigation and call dedicated solutions.

The test environment was not extremely challenging in the tests we performed. Overall results had reasonable performances during tests, as seen in Figure 2. In more challenging environments, car drivers still experience inaccurate GPS positioning when in deep canyons, in the presence of highly reflective buildings, or when driving at low speed, but this topic will not be covered in this article.

New GIS products coming onto the market offer opportunities to bring answers to the problem with a market-ready approach.