Using Digital Trajectory Indoor Localization Using Digital Trajectory Zhang Jingcong 5120309521 2015-06-02

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

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

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

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

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

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

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

My Research & Analysis trajectory building-using Android

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

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

Experiment & Result Trajectory building

Experiment & Result Kalman Filter truth observed filtered

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

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

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