Improved Urban Navigation with Collaborative Shadow

Matching and Specular Matching in FormationKirsten Strandjord, Penina Axelrad, Shan Mohiuddin

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Urban Challenges -Visibility

‒ A growing number of location-based services rely on GPS

technology for personal and commercial navigation.

‒ Expect these services to work reliably in all environments.

‒ Conventional methods for GPS positioning perform poorly.

‒ Tall, densely spaced buildings, block many of the direct line-of-

sight (DLOS) signals from GPS satellites.

‒ GPS user cannot obtain an accurate position solution.

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Urban Challenges - Geometry

‒ The geometry of the satellites that are visible is usually poor due

to the nature of urban canyons.

‒ Signals that have a LOS along the direction of the city street are

likely to be visible.

‒ Signals in the across-street direction are likely to be blocked by a

wall of tall buildings.

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Urban Challenges - Reflections

‒ GPS signal reflections prevalent due to the geometry of urban canyons

and the nature of the often highly reflective building materials

‒ The signal does not travel entirely along the DLOS vector direction but

the non-line-of-sight (NLOS) signal

‒ Reaches the receiver after it has been reflected or diffracted by a surface

in the environment

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Background –Shadow Matching (SM) BasicsTechnique that finds most likely user location using the GPS satellite visibility

predictions from the 3D model compared to the observed SNR values (Groves

2012)

Signal from the satellite visible to a receiver in some locations and blocked

in other locations within the canyon

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Background –SM General ProcedureSM techniques have shown results of meter-level accuracies in the across-

street direction in dense urban areas

1. Select a search radius around the a priori position solution.

2. Select a set of candidate locations

3. At each candidate location, use the building model to predict the visibility

of each satellite

4. Scoring scheme compares the visibility predictions to the signal strength

values.

5. Each candidate location receives a score that represents the likelihood

the GPS user is located at that particular position.

𝑓𝑝𝑜𝑠 𝑝 =

𝑠=1

𝑚

𝑓𝑠𝑎𝑡 𝑠, 𝑝, 𝑆𝑆

Score: 𝑓𝑝𝑜𝑠 candidate: 𝑝 scoring scheme: 𝑆𝑆 each satellite: 𝑠

𝑚: satellites above the horizon

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Background –SM Scoring Schemes

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𝑝 𝐿𝑂𝑆 𝑆𝑁𝑅 = 𝑠 =𝑙𝑠

𝑙𝑠 + 𝑛𝑠

𝑃𝑚 = 1 − 𝑝 𝐿𝑂𝑆 𝑆𝑁𝑅 = 𝑠 − 𝑏 + 2𝑏 𝑝 𝐿𝑂𝑆 𝑆𝑁𝑅 = 𝑠

𝑃𝑚, candidate score for each satellite;

b is 0.85 if the satellite is predicted to be visible and 0.2

otherwise

BINARY TERNARY

BAYESIAN Wang, et al., 2015

Wang, et al., 2012Groves

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Denver Downtown Characteristics

Denver’s rapid-growth, with a corresponding

increase in high-rise building developments, creates

complications for GNSS-dependent commuters.

December 2018 the buildings surrounding the stops

and stations along the metro commuter rail network

appeared to be causing GNSS signal cutouts

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Denver Experiment Locations

To get a realistic set of urban tracking results, we performed three experiments

(Exp1, Exp2, and Exp3) in downtown Denver

Software Structure and Inputs

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Visibility Metric & Specularity Metric

ത𝐿 = −2 ത𝑛 ∙ ഥ𝐷 ത𝑛 + ഥ𝐷

ത𝐿 ∙ ത𝑅 ≥ 0.99

Formulation of a specularity metric which is used and stored in sky plots similar

to shadow matching method.

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SNR & Specularity Metric

• GLONASS PRN 4 in Exp1, GPS PRN 10 in Exp2, and GPS PRN 5 in Exp3 are

all significantly blocked by buildings.

• Receivers record high SNR levels for these satellites at various times during the

experiment; which correlate with the prediction of a high likelihood of specularity

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SNR & Specularity Metric

Observed SNR vs specularity

metric for satellites with predicted

blockage of DLOS signals.

- Signals tracked with high SNRs are

correlated with high specularity metric

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Visibility Metric & Specularity Metric

In Exp2 and Exp3, though the receivers (R1-R4) are all near one another (within

several meters), the visibility and specularity sky plot vary between receivers.

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Results

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Results

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Specular Matching Results

The specular matching method

outperforms the conventional SM

scoring scheme for each receiver

for all experiments.

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Future Investigation

• Performing additional experiments in diverse regions in Denver using

higher performance receivers

• Focusing on the practical implementation of the SPM method, including

calculating the computation times and database requirements for the

generation and storage of specularity sky plots for all of downtown Denver

• Incorporating knowledge into the model regarding building materials or by

verifying reflective building surfaces through sampled or crowdsourced

SNR data

Thank you for your attention.

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Questions?

References

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