Outdoor Transmitter Localization - PowerPoint Presentation

Slide1

Outdoor Transmitter Localization

Danielle Ho, Ethel Baber, George

VormittagSlide2

Introduction - Definitions

Outdoor Transmitter Localization – Process of locating a signal in an outdoor environment without knowing the exact position of transmitterSlide3

Introduction - Definitions

Multipath Distortion Effect – reflection of radio signals off of obstructions which warp the signal being transmittedSlide4

Introduction - Definitions

Shadowing – the effect when wave signals circumvent the shadow of a building. Slide5

Introduction – O-CORNET

Cognitive radio network test bed designed at Virginia Tech

15 nodes + 2 mobile nodes

In the process of completionSlide6

Research Objective and Purpose

To lay the foundation for

others to use O-CORNET to research correlations between parameters and position

Cheaper/inexpensive

More accurate

Parameter: signal shape, length of signalSlide7

Applications

Large scale industrial use (cheap, cost effective)

Fraud detection

Emergencies: 911

Malicious attacks on systemSlide8

GNU Radio and USRP

GNU Radio

 is a free software development toolkit that provides signal processing blocks to implement software-defined radios and signal processing systems. It can be used with readily-available low-cost external RF hardware to create software-defined radios, or without hardware in a simulation-like environment.

USRP – Universal Software Radio Peripheral Slide9

Methodology

1) Transmitted and received signals

using USRPsSlide10

Methodology

2) Tested Industrial, Scientific, and Medical (ISM) BandwidthsSlide11

Methodology

3) Detected signal on O-CORNET from rogue

walkie

talkie transmitter

Walkie

Talkie:

Cobra

Microtalk

PR 260

Bandwidth:

467.5625 MHzSlide12

Methodology

4) Set up nodes to communicate with one another

Transfer Control Protocol (TCP) Sink/SourceSlide13

Methodology

5) Determined how to compile signal waves from multiple nodes onto one graph

TCP Source

WX GUI Scope SinkSlide14
Slide15

Methodology

6) Set

up nodes to store binary data into a file accessible by the laptop

Allows

for data to be analyzed at

a later timeSlide16

Methodology

7) Reduced

lag-time by streaming data to the laptop directly instead of routing it through the

nodes

8) Installed

Matlab

onto O-Cornet laptop

Was

used to analyze and compare the raw data from the

nodesSlide17

Methodology

9) Use python to convert raw binary data to MATLAB format

Analyze the data with MATLAB

Plot absolute value vs time

Plot frequency (scaled version of FFT plot)Slide18

Methodology

10) Data RunsSlide19

Methodology

11) Changed the sampling rate from 32k to 200k

Ran different data collection on the parking garage using Hahn 1, Hahn 2, and

Whittemore

nodesSlide20

Connection

Distance Apart

Run 1

to Hahn 1

273 m/896

ft

Run 1 to Hahn 2

241 m/786

ft

Run 1 to

Whittemore

139 m/458

ft

Run 2,3,4 to Hahn 1

269 m/863

ft

Run

2,3,4 to Hahn 2

228

m/748

ft

Run 2,3,4 to

Whittemore

162 m/532

ftSlide21

O-CORNET Tutorial

12) Wrote manual for O-CORNET for future researchers working with O-CORNET or continuing this projectSlide22

Obstacles

Unreliability of O-CORNET

C

annot consistently connect to nodes

Limited locations where we can receive signal from 3 nodes

O-CORNET nodes not time synchronized and no GPS implementedSlide23

Results and Conclusion

User Manual for O-CORNET

No major loss of data from file conversion

Code can be easily modified

Received signal parameters were consistent with expectations

Overall code preformed as intendedSlide24

Future Work

Replication of the study

Consistency and more reliability of O-CORNET

Proper software installed on all nodes

Hardware issues fixed

More accurate, higher power, and programmable transmitter

Determine if there is a correlation between signal shape or length and location