Statistical Analysis of Android GNSS Raw Data Measurements ...GNSS Raw Data Capability and Comparison with an Android Smartphone. Sensors. 18. 4185. 10.3390/s18124185. I. Exploring

The French Civil Aviation University

La référence aéronautique

Ecole Nationale de l’Aviation Civile

www.enac.fr

Réf: Version: Date:

1/21

1INC 19: Session A1: Innovative Navigation Solutions 1. Chair: Terry Moore

Statistical Analysis of Android GNSS Raw Data Measurements in

an Urban Environment for Smartphone Collaborative Positioning Methods

T. Verheyde , A. Blais , C. Macabiau , F.X. Marmet Telecommunication Research Laboratory– TéSA, Toulouse, France.

École Nationale Aviation Civile (ENAC) - Université de Toulouse, FranceCentre National d’Etudes Spatiales (CNES) – Toulouse, France

[thomas.verheyde],[blais],[macabiau]@recherche.enac.fr [email protected]

[1,2] [2] [2] [3]

[3]

[2]

[1]

1

2.0 Tuesday 21th November Authors: [1] Thomas VERHEYDE , [2] Antoine BLAIS, [2] Christophe Macabiau, [3] François-Xavier MARMET 22




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I. Introductiona. Contextb. Motivationsc. Objectives

II. Methodologya. Experimentation Protocolb. Clock Drift Estimationc. DLL Jitter Estimation d. PLL & Doppler Jitter Estimation

III. Results Analysis

IV. Conclusions

OUTLINE

2

22




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IV. Conclusions

OUTLINE

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I) Introduction - Context

• In May 2016, Google announced that GNSS raw data measurements will be available on Android smartphones via their latest API Android 7.0.

For the first time, developers and the scientific community will haveaccess to GNSS measurements from the smartphone integrated receiver.

Derived GNSS Measurementsobtained via Android

- Code measurements- Phase measurements- Doppler measurements- SNR measurements- Automatic Gain Control- and more …

Multiconstellation & Multifrequency

• A potential access to billions of GNSS smartphones data, leading to the use of advanced crowdsourcing GNSS processing techniques motivated my PhD thesis:

Precise Cooperative Positioning of Low-Cost Mobiles in an Urban Environment





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I) Introduction - Motivations

Android GNSS Raw Data Measurements Overview

Advantages Drawbacks

• Easy to record (GNSSLogger.apk or Geo++RinexLogger.apk)

• Support technological innovation

• Low-cost receiver

• Detailed documentation

• Analysis of the smartphone embedded GNSS algorithm

• Poor quality antenna

• Duty cycle

• Variety of smartphones models and brands

• Information on receiver architecture & tracking loop parameters are unknown





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6

I) Introduction - Motivations

[2] Gioia, Ciro & Damy, Sophie & Borio, Daniele. (2019). Precise Time in your Pocket: Timing Performance of Android Phones. 137-148. 10.33012/2019.16749.

[1] Lehtola, V. V., Söderholm, S., Koivisto, M., & Montloin, L. (2019). Exploring GNSS crowdsourcing feasibility: Combinations of measurements for modeling smartphone and higher end GNSS receiver performance. Sensors (Switzerland), 19(13), 1-17. [3018]. https://doi.org/10.3390/s19133018

[3] Lachapelle, Gérard & Gratton, Paul & Horrelt, Jamie & Lemieux, Erica & Broumandan, Ali. (2018). Evaluation of a Low Cost Hand Held Unit with GNSS Raw Data Capability and Comparison with an Android Smartphone. Sensors. 18. 4185. 10.3390/s18124185.

I. Exploring the feasibility of smartphone collaborative positioning [4]

II. How does a smartphone perform compared to a low-cost GNSS receiver in an urban environment. [2][3]

III. Smartphone GNSS chipset receiver architecture & tracking loop parameters are unknown [1]

[4] Garello, Roberto & Samson, Jaron & Spirito, Maurizio & Wymeersch, Henk. (2012). Peer-to-Peer Cooperative Positioning: Part II: Hybrid Devices with GNSS & Terrestrial Ranging Capability. Inside GNSS.





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I) Introduction - Objectives

In order to explore the feasibility of smartphone collaborative positioning, accurate and realistic simulations must be developed. Classic error models may not apply, embedded smartphone receivers need to be treated as a black box receiver.

Existing error models will be derived from real measurements processing.

• Characterizing smartphones GNSS receiver’s main parameters:

- Phase jitter (σ𝑷𝑳𝑳)- Code jitter (σ𝑫𝑳𝑳 )- Doppler jitter (σ𝑫𝒐𝒑𝒑𝒍𝒆𝒓)

- Clock drift (𝒄 ሶ𝜹𝒕)

• Find a possible correlation between those parameters and phone brands/models or GNSS embedded chipset brands/models.





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IV. Conclusions

OUTLINE

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II) Methodology - Protocols

Qualcomm Snapdragon 845

HiSilicon Kirin 980



BroadcomBCM47755

HiSilicon Kirin 980

Multi-ConstellationSingle Frequency

Multi-ConstellationMulti-Frequency




Google Pixel 3

Honor View 20

Xiaomi Mi 9

Xiaomi Mi 8

HuaweiMate 20X

11/201806/201804/201901/201911/2019

SamsungS10+

03/2019



x2 x39 tested smartphones

9





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II) Methodology - ProtocolsOn the roof On the roof

Google Pixel 3

Honor View 20

Xiaomi Mi 9

Geodetic GNSSAntenna

Honor View 20

Xiaomi Mi 8

HuaweiMate 20X

Inside the vehicle Inside the vehicle

Xiaomi Mi 8

OPEN

Geodetic GNSSAntenna

SBG Low-costIMU

Ublox F9Ublox F9

Ublox F9SPAN SPAN

ASTX_UGNSS Receiver

AsteRxGNSS Receiver

PolaRx5GNSS Receiver

10





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II) Methodology - Protocols

Data Collection Campaign

- Various collaborative scenarios between two vehicles

- Static scenarios for assessing nominal performance

- Total of collection time ≈ 10 hours

Mi 8Mi 9

View 20

Pixel 3

S10+

Data Collection Campaign Trajectory in Toulouse, FRANCE

Static Scenario (Open Sky Environment)

11

20cm


Rooftop Smartphones Configuration




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II) Methodology - Clock Drift Estimation

𝑑1𝑑2⋮𝑑𝑖

=

−𝑎𝑥1 −𝑎𝑦1 −𝑎𝑧1 1−𝑎𝑥2⋮

−𝑎𝑥𝑖

−𝑎𝑦2⋮

−𝑎𝑦𝑖

−𝑎𝑧2⋮

−𝑎𝑧𝑖

1⋮1

∙

𝑣𝑢𝑥𝑣𝑢𝑦𝑣𝑢𝑧𝑐 ሶ𝛿𝑡

+

휀 ሶ𝜌1휀 ሶ𝜌2

⋮휀 ሶ𝜌𝑖

ሶ𝜌𝑖𝑠𝑣 = 𝑣𝑥𝑖 ∙ 𝑎𝑥𝑖 + 𝑣𝑦𝑖 ∙ 𝑎𝑦𝑖 + 𝑣𝑧𝑖 ∙ 𝑎𝑧𝑖 + 𝑐 ሶ𝛿𝑡 + 휀 ሶ𝜌𝑖

𝑠

with ഥ𝑎𝑖 being the user-satellite line of sight vector

Solving this equation with a WLSE, we obtain 𝑐 ሶ𝛿𝑡.

Doppler measurement model:

Multiconstellation & Multifrequency

Hypothesis

12

Clock Drift for a Honor View 20


𝑑𝑖𝑠𝑣 = ሶ𝜌𝑖

𝑠𝑣 − 𝑣𝑖𝑠𝑣 ∗ 𝑎𝑖

𝑠𝑣

Setting that:

thus

𝑣𝑖𝑠𝑣 ∗ 𝑎𝑖

𝑠𝑣Predicted Radial Satellite Movement :

𝑐 ሶ𝛿𝑡 modeling : 𝑐 ሶ𝛿𝑡 = 𝑐 ሶ𝛿𝑡𝑓𝑎𝑠𝑡 + 𝑐 ሶ𝛿𝑡 𝑠𝑙𝑜𝑤

𝑐 ሶ𝛿𝑡𝑠𝑙𝑜𝑤 is modeled by a curve fitting

Static scenario : User position is computed by a reference receiver




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𝜌𝑖𝑠𝑣 − 𝜑𝑖

𝑠𝑣 = 2휀𝐼𝑜𝑛𝑜 + 휀𝐶𝑜𝑑𝑒 − 휀𝑃ℎ𝑎𝑠𝑒 + ∆휀𝑀𝑢𝑙𝑡𝑖𝑝𝑎𝑡ℎ + ∆𝜑

INC 19: Session A1: Innovative Navigation Solutions 1. Chair: Terry Moore

II) Methodology – DLL Jitter Estimation

𝜌𝑖𝑠𝑣 = 𝑟 + 𝑐 𝑡𝑟𝑥 − 𝑡𝑡𝑥 + 휀𝐼𝑜𝑛𝑜 + 휀𝑇𝑟𝑜𝑝𝑜 + 휀𝐶𝑜𝑑𝑒 + 휀𝑀𝑢𝑙𝑡𝑖𝑝𝑎𝑡ℎ

𝜌

𝜑𝑖𝑠𝑣 = 𝑟 + 𝑐 𝑡𝑟𝑥 − 𝑡𝑡𝑥 − 휀𝐼𝑜𝑛𝑜 + 휀𝑇𝑟𝑜𝑝𝑜 + ∆𝜑 + 휀𝑃ℎ𝑎𝑠𝑒 + 휀𝑀𝑢𝑙𝑡𝑖𝑝𝑎𝑡ℎ

𝜑

Best available satellite signal

Code model :

Phase model :

Code minus carrier (CMC) model :

13Galileo – PRN 21 – E1

DLL Jitter for a Honor View 20


𝑑

𝑑𝑡( 𝜌𝑖

𝑠𝑣 − 𝜑𝑖𝑠𝑣 ) =

𝑑

𝑑𝑡(2휀𝐼𝑜𝑛𝑜 + 휀𝐶𝑜𝑑𝑒)

CMC Time differenced model :

Hypothesis

∆휀𝑀𝑢𝑙𝑡𝑖𝑝𝑎𝑡ℎ ≈ 0

휀𝐶𝑜𝑑𝑒 ≫ 휀𝑃ℎ𝑎𝑠𝑒

Open sky scenario :

Error model :

Ionospheric variation:𝑑

𝑑𝑡( 휀𝐼𝑜𝑛𝑜 ) ≈ 0

Cycle slip detection: Android flag detection




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II) Methodology – PLL/FLL Jitter Estimation

Best available satellite signal

14

𝜑𝑖𝑠𝑣 = 𝑟 + 𝑐 𝑡𝑟𝑥 − 𝑡𝑡𝑥 − 휀𝐼𝑜𝑛𝑜 + 휀𝑇𝑟𝑜𝑝𝑜 + ∆𝜑 + 휀𝑃ℎ𝑎𝑠𝑒 + 휀𝑀𝑢𝑙𝑡𝑖𝑝𝑎𝑡ℎ

𝜑Phase model :


Galileo – PRN 21 – E1a

Figure: PLL Jitter for a Honor View 20

Triple time differenced Phase model [5] :

𝑑3𝜑𝑖𝑠𝑣

𝑑𝑡3= ∆𝑡3(𝑟 + 𝑐 𝑡𝑟𝑥 − 𝑡𝑡𝑥 ) − ∆𝑡3(휀𝐼𝑜𝑛𝑜) + ∆𝑡3(휀𝑇𝑟𝑜𝑝𝑜) + ∆𝜑 + 휀𝑃ℎ𝑎𝑠𝑒 + ∆𝑡3(휀𝑀𝑢𝑙𝑡𝑖𝑝𝑎𝑡ℎ

𝜑)

Hypothesis

∆휀𝑀𝑢𝑙𝑡𝑖𝑝𝑎𝑡ℎ ≈ 0Open sky scenario:

Ionospheric variation:𝑑

𝑑𝑡( 휀𝐼𝑜𝑛𝑜 ) ≈ 0

Tropospheric variation:𝑑

𝑑𝑡( 휀𝑇𝑟𝑜𝑝𝑜 ) ≈ 0

Static scenario :

Cycle slip detection: Android flag detection

∆𝑡3 𝑟 + 𝑐 𝑡𝑟𝑥 − 𝑡𝑡𝑥 ≈ 0




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IV. Conclusions

OUTLINE

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2.0 Tuesday 21th November Authors: [1] Thomas VERHEYDE , [2] Antoine BLAIS, [2] Christophe Macabiau, [3] François-Xavier MARMET

III) Results Analysis – 𝒄 ሶ𝜹𝒕

Receiver Septentrio PolaRX5S*

u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone

* Results obtained by Lethova. et al [1]

16

22

0.25

0.26

0.42

Results obtained by Lethova. et al [1] Smartphones Tested




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



16

22

0.25

0.26

0.42




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



16

22

0.25

0.26

0.42




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



16

22

0.25

0.26

0.42




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



16

22

0.25

0.26

0.42

Comparing phones with no phase measurements




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



16

22

0.25

0.26

0.42

Comparing phones’ performance to commercial GNSS receivers




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



16

22

0.25

0.26

0.42

Comparing similar smartphones




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝒄 ሶ𝜹𝒕 [𝒎/𝒔] < 0.03 0.14 0.18 4.72 0.61 0.16 3.93 0.44

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



16

22

0.25

0.26

0.42

Comparing similar chipset manufacturer




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III) Results Analysis – σ𝑷𝑳𝑳


u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑷𝑳𝑳 [𝒎] 0.002 0.003 0.8 NaN 0.008 0.018 NaN 0.003

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



17

22

0.018

0.014

0.008




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑷𝑳𝑳 [𝒎] 0.002 0.003 0.8 NaN 0.008 0.018 NaN 0.003

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



17

22

0.018

0.014

0.008





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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑷𝑳𝑳 [𝒎] 0.002 0.003 0.8 NaN 0.008 0.018 NaN 0.003

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



17

22

0.018

0.014

0.008





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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑷𝑳𝑳 [𝒎] 0.002 0.003 0.8 NaN 0.008 0.018 NaN 0.003

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



17

22

0.018

0.014

0.008

Comparing similar chipset manufacturer




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III) Results Analysis – σ𝑫𝑳𝑳


u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑫𝑳𝑳 [𝒎] 0.049 0.22 6.42 1.88 0.152 0.68 0.24 0.97

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



18

22

1.06

0.71

0.135




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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑫𝑳𝑳 [𝒎] 0.049 0.22 6.42 1.88 0.152 0.68 0.24 0.97

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



18

22

1.06

0.71

0.135





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u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑫𝑳𝑳 [𝒎] 0.049 0.22 6.42 1.88 0.152 0.68 0.24 0.97

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



18

22

1.06

0.71

0.135





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III) Results Analysis – σ𝑫𝒐𝒑𝒑𝒍𝒆𝒓


u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑫𝒐𝒑 [𝑯𝒛] N/A N/A N/A 4.79 0.23 0.10 1.27 0.05

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



19

22

0.11

0.14

0.19




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III) Results Analysis – σ𝑫𝒐𝒑𝒑𝒍𝒆𝒓


u-bloxM8T*

Samsung S8*

Google Pixel 3

Honor View 20

Xiaomi Mi 8

XiaomiMi 9

Samsung S10+

𝝈𝑫𝒐𝒑 [𝑯𝒛] N/A N/A N/A 4.79 0.23 0.10 1.27 0.05

2nd Phone

3rd Phone



HiSilicon Kirin 980


BroadcomBCM47755



19

22

0.11

0.14

0.19





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IV. Conclusions

OUTLINE

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22




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IV) Conclusions

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▪ Smartphone embedded GNSS receivers have been successfully characterized for a nominal performance scenario (open sky conditions).

▪ Estimated parameters for similar phones (same model) tend to be equals and therefore can be modeled.

▪ Further analysis need to be made in order to draw a clear correlation between the estimated parameters (DLL, PLL, Doppler jitters & Clock Drift) and GNSS chipset brands/models.

▪ A characterization method will be established for urban canyons environment

▪ Android Flags need a deeper investigation (cycle slip detection)




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IV) Conclusions - References

22

[2] Gioia, Ciro & Damy, Sophie & Borio, Daniele. (2019). Precise Time in your Pocket: Timing Performance of Android Phones. 137-148. 10.33012/2019.16749.

[1] Lehtola, V. V., Söderholm, S., Koivisto, M., & Montloin, L. (2019). Exploring GNSS crowdsourcing feasibility: Combinations of measurements for modeling smartphone and higher end GNSS receiverperformance. Sensors (Switzerland), 19(13), 1-17. [3018]. https://doi.org/10.3390/s19133018

[3] Lachapelle, Gérard & Gratton, Paul & Horrelt, Jamie & Lemieux, Erica & Broumandan, Ali. (2018). Evaluation of a Low Cost Hand Held Unit with GNSS Raw Data Capability and Comparison with an Android Smartphone. Sensors. 18. 4185. 10.3390/s18124185.

[4] Garello, Roberto & Samson, Jaron & Spirito, Maurizio & Wymeersch, Henk. (2012). Peer-to-Peer Cooperative Positioning: Part II: Hybrid Devices with GNSS & Terrestrial Ranging Capability. Inside GNSS.

[5] De Bakker, Peter & Samson, Jaron & Joosten, P & Spelat, M & Martin, Hollreiser & Ambrosius, B. (2006). EFFECTS OF RADIO FREQUENCY INTERFERENCE ON GNSS RECEIVER OUTPUT.




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36

Questions ?

INC 19: Session A1: Innovative Navigation Solutions 1. Chair: Terry Moore 2.0 Tuesday 21th November Authors: [1] Thomas VERHEYDE , [2] Antoine BLAIS, [2] Christophe Macabiau, [3] François-Xavier MARMET

Statistical Analysis of Android GNSS Raw Data Measurements in

an Urban Environment for Smartphone Collaborative Positioning Methods

T. Verheyde , A. Blais , C. Macabiau , F.X. Marmet Telecommunication Research Laboratory– TéSA, Toulouse, France.

École Nationale Aviation Civile (ENAC) - Université de Toulouse, FranceCentre National d’Etudes Spatiales (CNES) – Toulouse, France

[thomas.verheyde],[blais],[macabiau]@recherche.enac.fr [email protected]

[1,2] [2] [2] [3]

[3]

[2]

[1]

Thesis financed by:

Tuesday 21th November 2019 – INC 19 – Edinburgh

Documents

Statistical Analysis of Android GNSS Raw Data Measurements ...GNSS Raw Data Capability and Comparison with an Android Smartphone. Sensors. 18. 4185. 10.3390/s18124185. I. Exploring