ERU 3 Positioning

8/3/2019 ERU 3 Positioning

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Cellular Positioning

Shashika BiyanwilaResearch Engineer


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Outline

What is Cellular Positioning

Positioning Parameters Feasible Approaches Identified

Implementation

Some Trial Results

Future Approach of the Research


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What is cellular positioning ?

Determining the position of a Mobile Station(MS), using location sensitive parameters

Why ????

To provide Location Based Services


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Operator services

BillingNetwork managementLocation based servicesWireless Gaming

Assistance

Roadside assistance

Personal or vehicleemergencyAlarm managementDriving Directions

Tracking

Tracking criminalsTracking externalresources containers etc

Monitoring

Monitoring delivery processFleet & freight trackingPersonal Child SecurityMobile Worker management

Information

TrafficNearest servicenewsnavigation helpadvertisingInformation Directory

Applications of cellular positioning


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Cell-ID

Received Signal Strength Intensity (RSSI)

Timing Advance (TA) Uplink Time (Difference) Of Arrival (TDOA)

Downlink Observed Time Differences (E-OTD)

Angle of Arrival (AOA)

Positioning Parameters


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Feasible Approaches Identified

PositioningTechniques

1Geometrical

Approach

2Statistical

Approach

3DatabaseCorrelationApproach


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1. Geometrical Approach

Based on distancemeasurements

Two Steps:

- Distance calculation

- Location Estimation


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Geometrical approach contd..

Distance Calculation

- Measure the RSSI from neighboring cells

- Apply Propagation models to calculate the distance

Propagation Models

- Hata Model

- Extended Hata Model

- Lees Model- CCIR Model

- Walfisch-Ikegami Model (for micro cells)


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2. Statistical Approach

Construct a statistical propagation model for theRSSI

- Find RSSI at distance dfrom the transmitter

- Offsite calibration is necessary to estimate thepropagation parameters

Define a probability distributionfor the RSSI Location estimation problem is solved as an inverse

or, rather, inference problem


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Statistical Approach contd..

Log-loss or Log-distance model

Gaussian Probability Distribution

Propagation Parameter Estimation

- Maximum Likelihood estimation

Location Estimation

- Maximum A posteriori Probability


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Statistical Approach cntd..

Area being considered isdivided into several squares

A posterior probability of thelocation be within a square,is calculated for each square

Square with

Maximum A posteriorProbability


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3. Database correlation Method (DCM)

Involves a database of reference fingerprints for thewhole area of interest.

Fingerprint a recorded measurement sample from acertain location in the area

GPS coordinates of a location

RSSI (from available cells) in that location


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How to collectfingerprints?

By measurements

Using a Networkplanning tool

High samplingresolution is needed.

Measurement

Fingerprint

Test route

Fingerprint

DCM contd


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Location estimation

Compare the input measurement with reference fingerprints

- Using Cost Functions

Location of the best matching reference fingerprint

Estimated Location

Input

Measurement

DCMAlgorithm

Database

Estimated

Location

DCM contd


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Implementation

RSS

MeasurementUnit

RSSI + GPS

Reading

CommandsInterfacing Program

Database

Location Estimation Algorithm

Display Program

Software environment

Location ?

Hardware Environment


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Trial & Results

Urban

- Wellawaththa to

Kolpetty Suburban

- Katubedda toPiliyandala

Rural- Ibbagamuwa


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Urban area..

CDF wise comparison for Urban area

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

100 200 300 400 500 600 700 800 900 1000Error Less Than (m)

Percenta

ge Geometrical

Statistical

DCM

Existing Method


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Suburban area .

CDF wise comparison for Sub Urban area

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

100 200 300 400 500 600 700 800 900 1000 1500 2000 2500

Error Less than (m)

Percentage Geometrical

DCM

Existing Method

Statistical


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Rural area ..

CDF wise comparison for Rural area

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

1000 1250 1500 1750 2000 2250 2500 2750 3000 3500 4000 4500 5000Error Less Than (m)

Percentage Geometrical

DCM

Existing Method

Statistical


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Future Approach of the Research

Improvements to the current DCM approach

Drawbacks

- Few instances of poor estimations

- Creating, updating & maintenance of the database

How To Overcome

- Refined estimation techniques

- Use of a Network planning tool to createfingerprints


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Implementation of a positioning engine and associatedservices

Services

Get your own location

Track others web-based location on a map

GSM Network

EstimatedLocation

ReceivedSignal

Fingerprint

Location Estimation

using Received SignalFingerprint & database

System

Information

Calibration

Fingerprints

Digital MapsPositioningEngine


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Thank You

Documents

ERU 3 Positioning