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Sensor Fusion Study - Real World 2: GPS & INS Fusion [Stella Seoyeon Yang]
1. GPS Carrier Phase / INS Integrated
Smartphone Pedestrian Dead-Reckoning
Using User Context Classifying Deep Learning
SEO YEON YANG
2. Motivation and Background
(2016) Raw GNSS
Measurement
API Android is opened
After 7. nougat version
(2018) Xiaomi Dual
frequency Smarphone
Software and Hardware upgrade in Smartphone
3. Motivation and Background
GPS / INS integration with
Raw measurement
“Sample-level deep
convolutional neural networks
for music auto-tagging using
raw waveforms,”
Jongpil Lee, Jiyoung Park,
Keunhyoung Luke Kim and Juhan
Nam
“Sample-level CNN
architectures for music auto-
tagging using raw waveforms”
Taejun Kim, Jongpil Lee and Juhan
Nam
Smartphone User context classification
Cycle slip in Urban
environment
4. Contribution
• Detail Analysis of the Smartphone INS sensor property
• GPS carrier phase cycle slip detection and compensation
in smartphone platform
• Raw GPS / INS integrated pedestrian dead-reckoning
system construction
• User Pose Context classification performance analysis
with different preprocessing
7. INS sensor Properties Analysis
INS sensor Measurement model
: inertial to the body from the rotation matrix
: scale factor
: constant bias
: random bias
: Gaussian noise
: white noise
: constant bias
: random bias
: scale factor
Static experiment
Static experiment
Gravity, Turntable
experiment
Allan variance analysis
9. INS sensor Properties Analysis
Accel x Accel y Accel z Gyro x Gyro y Gyro z
0.0093 0.0094 0.0127 0.0013 0.0014 0.0012
Accel x Accel y Accel z
Pose 1 Mean -0.1691 -0.3070
Pose 2 Mean -0.1597 0.2352
Mean -0.1644 -0.3070 0.2352
Gyro x Gyro y Gyro z
1 experiment 0.0117 -0.0145 -0.0336
2 experiment 0.0120 -0.0143 -0.0344
Mean 0.01185 -0.0144 -0.034
13. Allan Variance Analysis, Random Bias
https://drive.google.com/file/d/1MTnLZK5xaAtnVU9H2qPGlj0JB
_9ykD5v/view?usp=sharing
https://drive.google.com/file/d/1B1WjtABBleWax1jmqvZ
MtrnlNXvK9jXw/view?usp=sharing
20. PDR Fitting experiment
PDR parameter fitting experiment
Length
(cm)
Heading
(deg)
Step
number
Track line 1 951 325 17
Track line 2 956 415 18
Track line 3 943 145 17
Track line 4 943 235 17
Times Each 3 turn , 5 times experiment is done
Place Seoul National University President Grace
Threshold is
choosed with
experimental result.
Std th. 0.3
24. Simulation enviromnet : cycle = 1000 * 5
1. 0~250 random position, 1cycle slip, ½
cycle slip injection
2. With threshold the cycle slip detected
1) cycle slip epoch is catched?
2) more than cycle slip is catched?
False Alam and Miss Detection
Cycle slip detection
26. GPS Raw measurement Navigation
Name Mean
Error (deg)
Mean
Error (deg)
Mean
Error (m)
Mean
Error (m)
NMEA 2.8095e-05 2.7539e-05 2.7805 2.5397
Psuedorange 6.1566e-05 5.5452e-05 9.3837 5.4730
Name North Vel. Mean
Error (m/sec)
East Vel. Mean
Error (m/sec)
Total Mean
Error (m/sec)
WLS doppler 0.1950 0.1692 0.1821
Not WLS
doppler
0.2048 0.1906 0.1977
carrier 0.1878 0.1674 0.17766
NMEA 0.1916 0.2427 0.21715
Position Determination with PR
Velocity Determination with CP, DP
31. GPS / INS integration
Measurement model
Process model Time Interpolation
Experiment with
Trimble GEO-XR as true track
Pos : NMEA
Vel : carrier vel
Heading : cp heading
33. Deep Learning Scheme
System diagram
<Data-preprocessing>
Smartphone sensors :
- Accelerometer
- Gyroscope
- Magnetometer
- Altimeter
- GPS
1) Sensor data
acquisition app
2) Calibration
<PDR>
Walking detection
Step counting
Stride length
Heading
Attitude Kalman
Pose Context PDR
<GPS/INS>
Cycle-slip detect
/compensate
Position Kalman
Error estimation
• User’s Walking
context
classification :
• GPS outage error estimation
deep learning:
MATLAB PART
TENSORFLOW PART
34. Dataset : Classification
Hand Held Hand Held Using Shirt Pocket
Trousers Front Pocket Trousers Back Pocket Backpack Handback
Walking Detection and
Step Counting on
Unconstrained
Smartphone :
Agata Brajdic
Open Dataset Class
35. Model 1
Time cutting
+ LSTM
Model 2
STFT
+ LSTM
Model 3
CNN
+ LSTM
Different Preprocessing
36. Type Model Prediction Loss
train Time domain LSTM 0.85 1.2
train STFT LSTM 0.95 1.1
train Time domain CNN
+ LSTM
0.92 1.15
test Time domain CNN
+ LSTM
0.75 1.32
Model 1 Model 2 Model 3
Training Result
Pred
Loss
37. The Galaxy S8 smartphone INS, bias, noise, scale factor, raw GPS
cycle slip property is detaily suggested.
Cycle slip elimination and
the raw GPS / INS integrated pedestrian dead reckoning getting
the high performance than NMEA
In position and velocity.
The smartphone’s walking context classification can know where
the phone is , what pose the user is.
Result
38. [1] Joonseong Gim ,; Kwan-dong Park; Comparison of Positioning Accuracy Using the Pseudorange from Android GPS
Raw Measurements. KONI 2017
[2] https://www.gsa.europa.eu/newsroom/news/world-s-first-dual-frequency-gnss-smartphone-hits-market
[3] Zhang, W.; Li, X. Wei, D.; Ji. X.; Yuan, H. A Foot-Mounted PDR System Based on IMU/EKF + HMM + ZUPT + ZARU +
HDR + Compass Algorithm. In Proceedings of the 2017 Internationl Conference on Indoor Positioning and Navigation(IPIN),
Sapporo, Japan, 18-21 September 2017.
[4] Shu, Y.; Bo, C.; Shen, G.; Zhao, C.; Li, L.; Zhao, F. Magicol : Indoor localization using pervasive magnetic field and
opportunistic WiFi sensing. IEEE J.Sel. Areas Commun. 2015. 2015, 33, 1443-1457
[5]. Li-Ta Hsu; Yanlei Gu; Yuyang Huang ; Shunsuke Kamijo; Urban Pedestrian Navigation Using Smartphone-Based
Dead Reckoning and 3-D Map-Aided GNSS. IEEE Sensors Journal Volume: 16 , Issue: 5 , March1, 2016
[6]. Tahmina Zebin ; Patricia J Scully ; Krikor B. Ozanyan; Human activity recognition with inertial sensors using a deep
learning approach. IEEE SENSORS 09 January 2017
Motion Recognition-Based 3D Pedestrian Navigation System Using Smartphone
Reference
39. [7]. Agata Brajdic ; Robert Harle ; Walking Detection and Step Counting on Unconstrained Smartphones UbiComp '13
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
[8]. Beomju Shin ; Chulki Kim ; Jaehun Kim ; Seok Lee; Motion Recognition-Based 3D Pedestrian Navigation System
Using Smartphone IEEE Sensors Council
[9]. Marcus Edel ; Enrico Köppe; An advanced method for pedestrian dead reckoning using BLSTM-RNNs. 2015
International Conference on Indoor Positioning and Indoor Navigation (IPIN)
[10] Ciftcioglu, Oe; Adaptive training of feedforward neural networks by Kalman filtering. GENERAL STUDIES OF
NUCLEAR REACTORS (E2400)
[11] Jiheon Kang; Joonbem Lee; Doo-Seop Eom; Smartphone-Based Traveled Distance Estimation Using Individual
Walking Patterns for Indoor Localization. 2018. August 13. IEEE Sensors
Remained Research