1. Using Digital Trajectory
Indoor Localization
Using Digital Trajectory
Zhang Jingcong

2. 1 2 3 4 Background & Related work My Research & Analysis OUTLINE
Experiment
& Result 4
Summary
& Future work

3. Using Digital Trajectory
Background & Related work
Indoor Localization
Using Digital Trajectory
TOPIC

4. Dead Reckoning Indoor Localization Using Digital Trajectory TOPIC
No accurate
Background & Related work
Dead Reckoning
Indoor Localization
Using Digital Trajectory
TOPIC

5. Indoor Localization GPS Fingerprinting Using Digital Trajectory TOPIC
No accurate
Background & Related work
GPS
No accuracy
Indoor Localization
Using Digital Trajectory
Fingerprinting
TOPIC
- Online:construction of RM
- Offline:localization based on RM
Wi-Fi RSS(received signal strength)
AP(access point)s
AP1
AP2
AP3

6. Indoor Localization GPS Fingerprinting Using Digital Trajectory TOPIC
Background & Related work
GPS
No accuracy
Indoor Localization
Using Digital Trajectory
Fingerprinting
TOPIC
noise
reflection
diffraction
etc.
channel
accuracy
density of fingerprints

7. with fewer fingerprint samples
My Research & Analysis
target
improve the accuracy
with fewer fingerprint samples
use Android smartphone embedded sensors to
build a digital trajectory
solution

8. WI-fi fingerprinting More accurate result Trajectory construction
My Research & Analysis
More accurate result
WI-fi fingerprinting
Particle Filter
Trajectory
construction

9. My Research & Analysis
trajectory building-using Android

10. orientation & acceleration
My Research & Analysis
trajectory building-using Android
DETECTPV
SENSORS
orientation & acceleration
ALGORITHM:DEAD RECKONING
INPUT:a location fix f=(fx,fy)
acceleration a=(ax,ay,az)
OUTPUT:trajectory tr={(x1,y1),(x2,y2),…}
while new a do
|a|=sqrt(ax^2+ay^2+az^2);
if detectPV(|a|)=true then
calculate distance l;
get theta θ;
tr.add((xprev+l*sinθ,yprev+l*θ));
end
using PV(peak-valley) detection to decide a step
digital trajectory

11. N Particle Filter State Obser- vation Kalman Filter Estimation
My Research & Analysis
Kalman Filter & Particle Filter
Particle
Filter
State
weight
Obser-
vation
Kalman
Filter
Estimation
variance
Obser-
vation N
State
Trajectory
WiFi fingerprint

12. Experiment & Result
Trajectory building

13. Experiment & Result
Kalman Filter
truth
observed
filtered

14. Generate a digital trajectory based on Android sensors
Summary & Future work
summary
Generate a digital trajectory based on Android sensors
Use Kalman fliter to get a better estimation
future
work
Do experiment with real scene
Use Particle fliter instead of Kalman filter
Improve the Android program

15. Reference
Yang Liu, Marzieh Dashti, Jie Zhang.Indoor Localization on Mobile Phone Platforms Using Embedded Inertial Sensors
Xiuming Zhang, Yunye Jin, Hwee-Xian Tan and Wee-Seng Soh.CIMLoc: A Crowdsourcing Indoor Digital Map Construction System for Localization

16. Q&A