Some optimizations of WiFi- Based Indoor Positioning Hanqing Liu - - PowerPoint PPT Presentation

some optimizations of wifi based indoor positioning
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Some optimizations of WiFi- Based Indoor Positioning Hanqing Liu - - PowerPoint PPT Presentation

Some optimizations of WiFi- Based Indoor Positioning Hanqing Liu Research Related 02 01 Background Work CONTENTS Our Follow-up 04 03 Project Work Research Background Research Background I ndoor localization The implement of


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Hanqing Liu

Some optimizations of WiFi- Based Indoor Positioning

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CONTENTS

Research Background

01

Related Work

02

Our Project

03

Follow-up Work

04

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Research Background

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Research Background

4 The implement of positioning in indoor environment.

I ndoor localization W HY?

The signal from the satellite is too weak.

How ?

Using wireless communication, base station positioning, inertial navigation to form indoor location positioning system

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Related Work

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Basic Concept

6 The basis of Wifi-based indoor positioning system! Can be predicted by a LDPL m odel.

Received Signal Strength I ndication( RSSI ) LDPL Model

If Pi and γi are known, then an RSS m easurem ent pij can be converted into the distance dij.

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A Basic Positioning Method

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Triangular positioning and LDPL Model

The LDPL Model provide a connection between the location of the user and RSS. The cross point does not always occur in real case!

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Some Advanced Scheme of Indoor Positioning

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Fingerprint-based positioning

 Offline phase: a site survey is conducted to collect the vectors of RSSI.  Online phase: a user samples or measures an RSSI vector at his/ her position and reports it to the server.

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Some Advanced Scheme of Indoor Positioning

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Peer Assisted Positioning

 The device broadcasts a special audio signal.  Distance between peers is calculate based on TOA.

SLAM

 The training phase  The operating phase

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Our Project

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1 1

Our indoor positioning based on RSS

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1 2

Stage 1: Triangular Positioning Only

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1 3

Stage 2: Triangular Positioning With Some Optimizations

1 .Estim ating position based on position history. 2 . Estim ating RSS value through Kalm an filtering

Prediction = last_est + kg* (rssi- last_est)

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Follow-up Work

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Follow-up work of project

1 .Pressure test 2 .Adjustm ent of API

W HY? To find Bottleneck!

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Pressure Test Bottleneck: 1 .Connection to DB 2 .Max Connection to Apache 3 . CPU

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THANKS