Shadow Matching
February 1, 2012 By: Paul D. Groves, Lei Wang, Marek Ziebart GPS WorldImproved GNSS Accuracy in Urban Canyons
Shadow matching. The two GNSS mobile phones beside the middle one show additional possible user positions referenced by the along-street component of the standard point positioning (SPP) solution.
GNSS positioning in dense urban areas is unreliable, with accuracy particularly poor in the cross-street direction. One solution is shadow matching, a new positioning technique that uses 3D building models to predict which satellites are visible from different locations and compares this with the measured satellite visibility to determine position. This article presents test results of a preliminary shadow-matching algorithm in a London urban canyon and discusses the practical implementation of the technique.
Poor GNSS positioning accuracy is common in urban canyons where tall buildings block the direct line-of-sight (LOS) signals from many, sometimes most, of the satellites, effectively casting GNSS shadows over the adjacent terrain. Without direct signals from four or more satellites, an accurate position solution cannot be determined. Sometimes, a degraded position solution can be obtained by using signals that can only be received by reflection off a building, known as non-line-of-sight (NLOS) signals.
Using GLONASS in addition to GPS considerably enhances direct signal availability, and the ongoing deployment of Galileo and Compass will enhance it further. However, an urban canyon affects the geometry of the available GNSS signals as well as their number. Signals with lines of sight going across the street are much more likely to be blocked by buildings than signals with lines of sight going along the street (see Figure 1). As a result, the signal geometry, and hence the positioning accuracy, will be much better along the direction of the street than across the street. For example, for a building-height-to-street-width ratio of three and direct signals from four GNSS constellations, the cross-street position uncertainty can exceed 20 meters, while the along-street uncertainty is within 5 meters.
Figure 1. Signal geometry of GNSS satellites in an urban canyon (aerial perspective).
This level of accuracy is good enough for some applications but not others. Knowing which side of the street a pedestrian on is useful for visitor guidance and location-based advertising, while it is critical for guiding the blind and visually impaired and for augmented-reality applications. Similarly, lane-level positioning is important for advanced intelligent transportation systems that can direct individual vehicles in order to maximize traffic flow and prioritize emergency vehicles.
Improving GNSS positioning in urban canyons requires lateral thinking. If it’s not possible to calculate a sufficiently accurate position solution using the visible satellites, why not use the nonvisible satellites as well? This is exactly what shadow matching does. If you know where the buildings are and how big they are, you can deduce positional information from the knowledge that certain signals are blocked.
This requires a 3D model of a city’s buildings. These are becoming more accurate and widely available and have already been used to predict GNSS signal availability and multipath interference.
The principle of shadow matching is simple. Due to obstruction by buildings in urban canyons, signals from many GNSS satellites will be receivable in some parts of a street, but not others. Where each direct signal is receivable can be predicted using a 3D city model. Consequently, by determining whether a direct signal is being received from a given satellite, the user can localize their position to within one of two areas of the street. Figure 2 illustrates this. By considering other satellites, the position solution may be refined further, producing a much more accurate cross-street position solution than available from conventional GNSS positioning in this environment. Thus the observed signal shadowing is matched with the predicted shadowing to determine position.
Figure 2. The shadow-matching concept: using direct signal reception to localize position.
This concept of shadow matching, has been proven by mathematical modeling. Satellite visibility predictions using a 3D city model of London have been validated with real-world observation, demonstrating the practical potential of shadow matching. Here, shadow matching is brought from proof of concept one step further to practical demonstration. A preliminary but complete implementation of shadow matching has been developed and tested in London using real-world GPS and GLONASS measurements. The algorithm is described first, followed by the test results. We then discuss dealing with different types of signal propagation that occur in urban areas. and how to implement shadow matching in real time on a platform such as a smartphone.
Shadow-Matching Algorithm
A basic shadow matching algorithm may be broken down into four steps:
- Perform standard point positioning (SPP) using GNSS pseudo-ranges to obtain an approximate user position.
- Define the search area for the shadow-matching position solution, generating a set of possible user positions close to the approximate position solution.
- Predict satellite visibility at each candidate position using the 3D city model.
- Evaluate the similarity between predicted and observed satellite visibility at each position. The candidate position with the best match is deemed to be the shadow-matching solution. This process can be conducted epoch by epoch, so the GNSS user can be either static or dynamic.
Conventional Positioning. In the first step, SPP using GNSS pseudo-ranges is conducted to acquire an initial user position. In an urban environment, the accuracy will often be poor, partly due to contamination by NLOS signals. Consistency checking may be used to identify the NLOS signals and, where possible, remove them from the position solution.
Candidate Position Determination. As discussed earlier, signal geometry and hence positioning accuracy will be much better along the direction of the street than across the street. Therefore, in this preliminary shadow-matching algorithm, the along-street component of SPP solution is used as a reference to generate a set of possible user positions that vary in across-street direction only (shown by the two mobile phones beside the SPP solution on the opening page of this article).
A more advanced shadow-matching algorithm would also consider candidate positions in the along-street direction and would vary the size of its search area based on an assessment of the quality of the SPP solution. The smaller the search area, the more efficient the shadow-matching algorithm will be. However, the search area must be large enough to contain the true position. Further research is needed to determine the optimum search area.
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