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Journal ArticleDOI

Shadow Matching: A New GNSS Positioning Technique for Urban Canyons

01 Jul 2011-Journal of Navigation (Cambridge University Press)-Vol. 64, Iss: 03, pp 417-430
TL;DR: In this article, the authors used 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position.
Abstract: The Global Positioning System (GPS) is unreliable in dense urban areas, known as urban canyons, which have tall buildings or narrow streets. This is because the buildings block the signals from many of the satellites. Combining GPS with other Global Navigation Satellite Systems (GNSS) significantly increases the availability of direct line-of-sight signals. Modelling is used to demonstrate that, although this will enable accurate positioning along the direction of the street, the positioning accuracy in the cross-street direction will be poor because the unobstructed satellite signals travel along the street, rather than across it. A novel solution to this problem is to use 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position. Modelling is used to show that this shadow matching technique has the potential to achieve metre-order cross-street positioning in urban canyons. The issues to be addressed in developing a robust and practical shadow matching positioning system are then discussed and solutions proposed.

Summary (2 min read)

1. I N T R O D U C T I O N. Poor performance of Global Positioning System

  • Section 2 presents modelling results to support this, showing how the availability in However, an urban canyon affects the geometry as well as the number of the available GNSS signals.
  • 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.
  • The shadow-matching positioning method reverses this by using the signal availability determined by the receiver to work out the user location.
  • These include handling database errors, the effect of along-street position errors on shadow matching, reliable determination of whether a satellite is directly visible and efficient preparation, dissemination and storage of the building database.

2. A V A I L A B I L I T Y M O D E L L I N G.

  • A series of simulations were run to determine the impact of GNSS constellation size and urban canyon aspect ratio on GNSS position solution availability.
  • In two scenarios, the street was aligned north-south and in the remaining four, it was aligned east-west.
  • For each aspect ratio, the satellite constellation was stepped through a 24 hour period in 60 s increments and the percentage availability of four or more direct LOS signals calculated.
  • Within the east-west plane, a greater proportion of the satellites have lower elevations, whereas within the north-south plane, the distribution is more even, with a peak around 77x.
  • Also, a good position solution may often be obtained from three direct LOS signals and a terrain height database.

3. C O N V E N T I O N A L P O S I T I O N I N

  • The performance of multi-constellation GNSS positioning in urban canyons using conventional methods may be predicted by multiplying the average dilutions of precision (DOP) by an estimated user equivalent range error (UERE).
  • Also, the path delay can be up to twice as large where the direct LOS signal is received through a gap between buildings.
  • Values are omitted for solution availabilities below 10% to avoid misleading outlier effects.
  • 5 because the ionosphere, troposphere and multipath error standard deviations will be larger than assumed in the UERE estimate.
  • As can be seen from the figures, the cross-street positioning accuracy is slightly poorer than its along-street counterpart for the north-south street alignment and substantially poorer for the east-west street alignment.

4. A S I M P L E S H A D O W -M A T C H I N G A L G O R I T H M.

  • The principle of shadow matching is simple.
  • Note that satellites that are either directly visible throughout the street or obstructed throughout the street do not contribute positional information.
  • Figure 8 depicts a simple shadow-matching algorithm.
  • To determine the performance of this shadow matching algorithm, it was tested using the 100-satellite constellation over each of the six scenarios used for determining urban canyon position solution availability and conventional positioning performance in the preceding sections.
  • Position containment is the difference between the maximum and minimum cross-street position as determined by the shadow matching process.

Boundaries of all-satellite localisation region

  • Figure 9 (left) shows the root mean square (RMS) cross-street position error using shadow matching as a function of street aspect ratio.
  • Performance in the car scenarios was poor for the lowest aspect ratio of 0 .
  • 1 because the GNSS shadows did not extend to the middle of the street.
  • The results presented here represent a lower bound on the expected accuracy of a practical shadow-matching system.
  • Additional position errors will occur due to errors in the 3D city model, the conventional GNSS position solution used to select the correct region of the model and the ability of the GNSS user equipment to determine whether a received signal is direct line of sight.

5. P R A C T I C A L S H A D O W M A T C H I N G. A practical implementation of shadow matching requires:

  • 3D city models are now available from a range of suppliers for cities all around the world.
  • As a strongly reflected signal will have left-handed circular polarisation (LHCP), a receive antenna with a good polarisation discrimination will help reduce its C/N 0 .
  • Cross-street errors in the positions of buildings in the database directly translate into errors in the boundary between the receivable and non-receivable regions.
  • It has been shown, using mathematical modelling, that although combining GPS with other GNSS significantly increases signal availability in urban canyons, the positioning accuracy in the cross-street direction will generally be poor because the unobstructed satellite signals travel along the street, rather than across it.
  • Issues affecting a practical implementation of shadow matching, such as database dissemination, satellite visibility determination and handling of database errors, have been explored and mitigating action proposed.

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Shadow Matching: A New GNSS
Positioning Technique for
Urban Canyons
Paul D. Groves
(University College London)
(Email: p.groves@ucl.ac.uk)
The Global Positioning System (GPS) is unreliable in dense urban areas, known as urban
canyons, which have tall buildings or narrow streets. This is because the buildings block
the signals from many of the satellites. Combining GPS with other Global Navigation
Satellite Systems (GNSS) significantly increases the availability of direct line-of-sight signals.
Modelling is used to demonstrate that, although this will enable accurate positioning along
the direction of the street, the positioning accuracy in the cross-street direction will be poor
because the unobstructed satellite signals travel along the street, rather than across it. A novel
solution to this problem is to use 3D building models to improve cross-track positioning
accuracy in urban canyons by predicting which satellites are visible from different locations
and comparing this with the measured satellite visibility to determine position. Modelling is
used to show that this shadow matching technique has the potential to achieve metre-order
cross-street positioning in urban canyons. The issues to be addressed in developing a robust
and practical shadow matching positioning system are then discussed and solutions
proposed.
KEY WORDS
1. GNSS. 2. Digital Maps. 3. Urban Canyon.
1. I N T R O D U C T I O N. Poor performance of Global Positioning System
(GPS) user equipment in urban canyons is a well-known problem [1–4]. Where
there are tall buildings or narrow streets, the direct line-of-sight (LOS) signals from
many, sometimes most, of the satellites are blocked. The buildings effectively cast
GPS shadows over the adjacent terrain. Figure 1 illustrates this. Without direct sig-
nals from four or more satellites, an accurate position solution cannot be deter-
mined. Sometimes, a degraded position solution may be obtained by making use of
signals that can only be received by reflection off a building ; these are known as
non-line-of-sight (NLOS) signals [5, 6].
As more satellites are deployed from the other Global Navigation Satellite System
(GNSS) constellations, the number of direct LOS signals available to suitably
equipped users in urban canyons is increasing. Consequently, the proportion of the
time for which four or more direct LOS signals are available is also increasing.
Section 2 presents modelling results to support this, showing how the availability in
THE JOURNAL OF NAVIGATION (2011), 64, 417–430. f The Royal Institute of Navigation
doi:10.1017/S0373463311000087

mid-latitude urban canyons varies as a function of the building height to street width
ratio. Three satellite constellations are modelled :
A 27-satellite constellation, assuming that only GPS signals may be received ;
A 65-satellite constellation assuming GPS, GLONASS and a partially-complete
Galileo constellation are available ;
A 100-satellite constellation assuming availability of GPS, GLONASS, Galileo
and Compass.
However, an urban canyon affects the geometry as well as the number of the
available GNSS signals. 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. This is illustrated by Figure 2. 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. Section 3 presents modelling results in support of this, showing how the
average along-street and cross-street positioning error with the 100-satellite constel-
lation vary with the building height to street width ratio.
Thus, although it should be possible to identify the correct street with multi-
constellation conventional GNSS positioning, determining where the user is across
that street will be difficult in deep urban canyons. However, there are many applica-
tions that benefit from this information. Traffic lane identification is useful for route
Figure 1. Illustration of signal blockage in an urban canyon (based on [3]).
Figure 2. Signal geometry in an urban canyon (aerial perspective).
418 P A UL D. G R OVE S V O L. 64

guidance an d vehicle tracking and is essential for lane-based road user charging.
Pedestrian route guidance is enhanced by knowing which side of the street an indi-
vidual is on; for visually impaired users, this is essential. Location-based directory
services can estimate the journey time to a restaurant or cafe
´
more accurately if they
know which side of the street the user is on. Accurate cross-street positioning is also
important for locating mil itary, security and emergency service personnel.
3D city models have been used to predict GNSS signal availability (and multipath
interference) in urban areas [7] and as part of the route-guidance user interface for
pedestrian navigation [8]. The shadow-matching positioning method reverses this by
using the signal availability determined by the receiver to work out the user locat ion.
At the correct location, the direct LOS signals recei ved will be unsha dowed according
to the city model and the remai ning signals will be shadowed. The conventional
GNSS positioning solution is used to limit the extent of the search region. Section 4
describes a simple shadow-matching algorithm and presents simulation test results
that demonstrate the concept and estimate the best-case performance.
To move from proof of concept to a practical shadow-matching positioning system,
a number of issues mu st be resolved. These include handling database errors, the
effect of along-street position errors on shadow matching, reliable determination of
whether a satellite is directly visible and efficient preparation, dissemination and
storage of the building database. Se ction 5 describes these challenges and discusses
possible solutions. Finally, Section 6 presents conclusions.
2. A V A I L A B I L I T Y M O D E L L I N G. A series of simulations were run to
determine the impact of GNSS constellation size and urban canyon aspect ratio on
GNSS position solution availability. The aspect ratio is the ratio of the building
height to the street wi dth. A position solution was assumed to be available wher-
ever direct LOS signals were receivable from four or more satellites.
Six scenarios were simulated for each satellite constellation , all with the user located
at latitude 45x, longitude 0. In two scenarios, the street was aligned north-south and
in the remaining four, it was aligned east-west. In half the scenarios, the receiver was
located 0
.
1 street widths from the edge, representing a pedestrian; in the remaining
scenarios, it was located 0
.
35 street widths from the edge, repres enting a car. For the
east-west street, scenarios for pedestrians and cars on both the north and south sides
of the street were simulated. For the north-south street, the receiver was locat ed on
the west side of the street only as east side results are very similar.
For each scenario, the building height to street width aspect ratio was varie d be-
tween 0 and 4 in steps of 0
.
1. For each aspect ratio, the satellite constellation was
stepped through a 24 hour period in 60 s increments and the percentage availability of
four or more direct LOS signals calculated. The elevation masking angle was 15x.
As only an approximate indication of signal availability is required, a number of
simplifying assumptions were made:
All satellites are evenly distributed amongst six evenly-spaced circular orbits,
inclined at 55x to the equator with a period of half a sidereal day;
The urban canyon has uniform width with no gaps between the buildings on
either side;
All buildings in the urban canyon have uniform height ;
The street is sufficiently long for end effects not to be considered.
NO. 3 SHADOW MATCHING 419

Figure 3 presents the availability results for the 27-, 65- and 100-satellite constella-
tions. As might be expected, the availability decreases with the urban canyon aspect
ratio and increases with the size of the satellite constellation. Less obviously, the
availability is significantly poorer along the north-south street than along the east-
west street. This can be explained by comparing the projections of the line of sight
vectors in the east-west and north-south planes. Within the east-west plane, a greater
proportion of the satellites have lower elevations, whereas within the north-south
plane, the distribution is more even, with a peak around 77x. Furthermore, for the
east-west street satellite availability is actually better on the south side of the street
than the north ; this is because there are more higher-elevation satellites to the north
than to the south. Figure 4 shows the west-east and north-south elevation distribu-
tions for satellites in GPS orbits.
In a real urban canyon, availability will be enhanced where satellites are visible
through gaps between buildings and diminished where satellites are blocked by tall
buildings in other streets. In particular, availability will typically be better in street
intersections.
In practice, where fewer than four direct LOS signals are available, a degraded
position solution can be often be obtained using NLOS signals. Also, a good position
solution may often be obtained from three direct LOS signals and a terrain height
database. Finally, dead reckoning [9] could be used to bridge a navigation solution
between intersections, though the accuracy will depend on sensor quality and en-
vironment.
Figure 3. Availability of direct LOS signals from four or more satellites in an urban canyon with
(top left) a 27-satellite; (top right) a 65-satellite; and (bottom) a 100-satellite constellation.
420 P A UL D. G R OVE S V O L. 64

3. CONVENTIONAL POSITIONING PERFORMANCE. The per-
formance of multi-constellation GNSS positioning in urban canyons using conven-
tional methods may be predicted by multiplying the average dilutions of precision
(DOP) by an estimated user equivalent range error (UERE). As for the availability
modelling, it is assumed that only signals from satellites with a direct line of sight
are used.
For each simulation, the LOS vectors were transformed to a coordinate frame
aligned with the along-street, cross-street and vertical axes. Along-street and cross-
street DOP wer e then calcul ated using the conventional method [9, 10]. For the
north-south street, the along-street DOP is the same as the north DOP and the cross-
street DOP is equal to the east DOP; for the east-west street, these are reversed. For
each of the six scenarios simulated for each constellation in the availability modell ing,
DOPs for each aspect ratio were averaged over a 24 hour period. Instances for which
fewer than four direct LOS signals were available were omitted from the averaging.
Instances where the relevant component of DOP exceeded 20 were also omitted to
prevent outliers from skewing the results.
The UERE esti mation was based on the following assumptions [9] :
A code tracking noise standard deviation (SD) of 0
.
67 m ;
A signal in space (residual orbit and satellite clock) error SD of 1
.
0m;
A residual ionosphere error SD of 2
.
0 m, assuming a single-frequency user and a
bias in favour of high-elevation satellites;
A residual troposphere error SD of 0
.
5 m, assuming a bias in favour of high-
elevation satellites;
A multipath error SD of 1
.
0m.
These give a total UERE of 2
.
6 m. The reason that the assumed multipath error SD is
so low is that the buildings block many reflected signal paths as well as direct signal
paths. For an urban canyon with buildings of equal height on both sides, the maximum
path delay for a singly reflected signal is w
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1+4b
2
=
w
2
p
, where w is the street width
and b the building height. Furthermore, the reflected to direct signal amplitude
0
20 40 60 80 100120140160 180
Elevation,
Relative density
Relative density
° (0 is due West, 180 due East)
West-East Elevation Distribution
0
Elevation, ° (0 is due North, 180 due South)
North-South Elevation Distribution
0
0.2
0.4
0.6
0.8
1.0
0
0.2
0.4
0.6
0.8
1.0
20
40 60 80 100120140
160
180
Figure 4. West-east and north-south elevation distributions for satellites in GPS orbits and a
receiver at 45x latitude.
NO. 3 SHADOW MATCHING
421

Citations
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Abstract: Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.

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TL;DR: Three different techniques for mitigating the impact of non-line-of-sight (NLOS) reception and multipath interference on position accuracy without using additional hardware are investigated, testing them using data collected at multiple sites in central London.
Abstract: Multiple global navigation satellite system (GNSS) constellations can dramatically improve the signal availability in dense urban environments. However, accuracy remains a challenge because buildings block, reflect and diffract the signals. This paper investigates three different techniques for mitigating the impact of non-line-of-sight (NLOS) reception and multipath interference on position accuracy without using additional hardware, testing them using data collected at multiple sites in central London. Aiding the position solution using a terrain height database was found to have the biggest impact, improving the horizontal accuracy by 35% and the vertical accuracy by a factor of 4. An 8% improvement in horizontal accuracy was also obtained from weighting the GNSS measurements in the position solution according to the carrier-power-to-noise-density ratio (C/N0). Consistency checking using a conventional sequential elimination technique was found to degrade horizontal positioning performance by 60% because it often eliminated the wrong measurements in cases when multiple signals were affected by NLOS reception or strong multipath interference. A new consistency checking method that compares subsets of measurements performed better, but was still equally likely to improve or degrade the accuracy. This was partly because removing a poor measurement can result in adverse signal geometry, degrading the position accuracy. Based on this, several ways of improving the reliability of consistency checking are proposed.

175 citations


Cites methods from "Shadow Matching: A New GNSS Positio..."

  • ..., 2012) and the generation of an intelligent urban positioning solution by combining augmented conventional positioning with the shadow-matching technique (Groves, 2011; Wang et al., 2012)....

    [...]

  • ...…using a 3D city model, when the user position is only approximately known, as discussed in (Groves et al., 2012) and the generation of an intelligent urban positioning solution by combining augmented conventional positioning with the shadow-matching technique (Groves, 2011; Wang et al., 2012)....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the authors used 3D building models to predict satellite visibility in urban canyons and evaluated the performance of current and future GNSS in London with decimetre-level accuracy.
Abstract: Positioning using the Global Positioning System (GPS) is unreliable in dense urban areas with tall buildings and/or narrow streets, known as ‘urban canyons’. This is because the buildings block, reflect or diffract the signals from many of the satellites. This paper investigates the use of 3-Dimensional (3-D) building models to predict satellite visibility. To predict Global Navigation Satellite System (GNSS) performance using 3-D building models, a simulation has been developed. A few optimized methods to improve the efficiency of the simulation for real-time purposes were implemented. Diffraction effects of satellite signals were considered to improve accuracy. The simulation is validated using real-world GPS and GLObal NAvigation Satellite System (GLONASS) observations. The performance of current and future GNSS in urban canyons is then assessed by simulation using an architectural city model of London with decimetre-level accuracy. GNSS availability, integrity and precision is evaluated over pedestrian and vehicle routes within city canyons using different combinations of GNSS constellations. The results show that using GPS and GLONASS together cannot guarantee 24-hour reliable positioning in urban canyons. However, with the addition of Galileo and Compass, currently under construction, reliable GNSS performance can be obtained at most, but not all, of the locations in the test scenarios. The modelling also demonstrates that GNSS availability is poorer for pedestrians than for vehicles and verifies that cross-street positioning errors are typically larger than along-street due to the geometrical constraints imposed by the buildings. For many applications, this modelling technique could also be used to predict the best route through a city at a given time, or the best time to perform GNSS positioning at a given location.

150 citations


Cites background or methods from "Shadow Matching: A New GNSS Positio..."

  • ...Another solution is GNSS shadow matching, which can potentially improve the across-street positioning accuracy by comparing the observed GNSS signal availability with that predicted using a 3-D city model (Groves, 2011; Wang et al., 2011; Groves et al., 2012)....

    [...]

  • ...D city model (Groves, 2011; Wang et al., 2011; Groves et al., 2012)....

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Journal ArticleDOI
11 Jan 2013-Sensors
TL;DR: Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article, with very first promising full-scale test results.
Abstract: Reliable GPS positioning in city environment is a key issue: actually, signals are prone to multipath, with poor satellite geometry in many streets. Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article. A virtual image processing that detects and eliminates possible faulty measurements is the core of this method. This image is generated using the position estimated a priori by the navigation process itself, under road constraints. This position is then updated by measurements to line-of-sight satellites only. This closed-loop real-time processing has shown very first promising full-scale test results.

127 citations

Journal ArticleDOI
TL;DR: In this article, shadow matching has been adapted to work on an Android smartphone and presented the first comprehensive performance assessment of smartphone GNSS shadow matching, which significantly improves cross-street positioning accuracy in dense urban environments.
Abstract: Global Navigation Satellite System (GNSS) shadow matching is a new positioning technique that determines position by comparing the measured signal availability and strength with predictions made using a three-dimensional (3D) city model. It complements conventional GNSS positioning and can significantly improve cross-street positioning accuracy in dense urban environments. This paper describes how shadow matching has been adapted to work on an Android smartphone and presents the first comprehensive performance assessment of smartphone GNSS shadow matching. Using GPS and GLONASS data recorded at 20 locations within central London, it is shown that shadow matching significantly outperforms conventional GNSS positioning in the cross-street direction. The success rate for obtaining a cross-street position accuracy within 5 m, enabling the correct side of a street to be determined, was 54·50% using shadow matching, compared to 24·77% for the conventional GNSS position. The likely performance of four-constellation shadow matching is predicted, the feasibility of a large-scale implementation of shadow matching is assessed, and some methods for improving performance are proposed. A further contribution is a signal-to-noise ratio analysis of the direct line-of-sight and non-line-of-sight signals received on a smartphone in a dense urban environment.

116 citations


Cites background or methods from "Shadow Matching: A New GNSS Positio..."

  • ...Consequently, the signal geometry, and hence the positioning accuracy, is much better along the direction of the street than across the street (Groves, 2011)....

    [...]

  • ...3 Figure 2: The shadow-matching concept: using direct signal reception to localise position (Groves, 2011)....

    [...]

  • ...Shadow matching is a new positioning technique using GNSS, assisted by knowledge derived from 3D city models, that has the potential to provide metres-level cross-street accuracy in urban canyons (Groves, 2011; Tiberius and Verbree, 2004)....

    [...]

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Frequently Asked Questions (9)
Q1. What are the contributions in "Shadow matching: a new gnss positioning technique for urban canyons" ?

In this paper, the authors used 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position. 

dead reckoning [9] could be used to bridge a navigation solution between intersections, though the accuracy will depend on sensor quality and environment. 

A practical implementation of shadow matching requires:’ Efficient preparation, dissemination and storage of the city models ; ’ Reliable determination of whether a satellite is directly visible ; and ’ Quantification and mitigation of errors. 

One way of mitigating these along-street errors is to repeat the shadow matching calculation for a number of different along-street positions, applying a consistency check in each case. 

where only one path is blocked, an erroneous shadow match will occur, whereby the shadow matching algorithm assumes a blocked signal when the true signal is available or an available signal when the true signal is blocked. 

In half the scenarios, the receiver was located 0.1 street widths from the edge, representing a pedestrian; in the remaining scenarios, it was located 0.35 street widths from the edge, representing a car. 

For applications where the antenna is kept (approximately) horizontal, the angle of incidence at the antenna will be known, so antenna gain calibration can be used to determine the expected C/N0. 

The performance of multi-constellation GNSS positioning in urban canyons using conventional methods may be predicted by multiplying the average dilutions of precision (DOP) by an estimated user equivalent range error (UERE). 

They are also omitted for building height to street width aspect ratios below 0.5 because the ionosphere, troposphere and multipath error standard deviations will be larger than assumed in the UERE estimate.