AN APPROACH TO WIRELESS INDOOR

POSITIONING SYSTEM

Senior Thesis in Electical Engineering

University of Illinois at Urbana-Champaign

Advisor: Prof. Jose E. Schutt-Aine

By

Jieping Wang

May 2016

ii

Abstract

This research project focuses on developing a wireless indoor positioning system that can

potentially be commercialized for civil use. Compared to the outdoor positioning systems that are

more fully developed with products that produce navigation solutions, the lack of existing stable,

robust and precise wireless indoor positioning systems aroused our interest in developing one.

Such a system can locate objects or people inside a building and can be extremely useful for users

such as malls or hospitals to provide navigation aid or emergency services.

Among all the methods available, we chose to use the phase difference calculation method. The

signal transmitted from the client side will first be mixed with the crystal oscillator from the server

side, and then pass through a notch filter that filters out the signal with higher frequency, and

eventually be transmitted back to the receiver from the client side. Since there is a phase difference

between the transmitted and the received signal from the client side, we are able to figure out the

distance between the client and server and thus provide the location of the client. Our method

proved to work with several wire-connected experiments. In order to visualize and extract the

phase difference in a wireless environment, we connected two antennas to the network analyzer

and analyzed the phase of S21 in ADS (Advanced Design System).

Subject Keywords: Wireless; indoor positioning; phase difference; antenna

iii

CONTENTS

CHAPTER 1 INTRODUCTION ................................................................................................................. 1

CHAPTER 2 GOALS, LITERATURE REVIEW AND APPROACHES ................................................. 3

2.1 Goals ................................................................................................................................................... 3

2.2 TDOA & TOA .................................................................................................................................... 4

2.3 UWB ................................................................................................................................................... 6

2.4 FMCW ................................................................................................................................................ 8

2.5 Previous Work................................................................................................................................... 10

2.6 Conclusion ........................................................................................................................................ 13

CHAPTER 3 SYSTEM STRUCTURE AND SIMULATION .................................................................. 14

3.1 Our System ........................................................................................................................................ 14

3.2 Simulation Using Matlab .................................................................................................................. 17

3.3 Matlab Accuracy Verification ........................................................................................................... 19

CHAPTER 4 WIRED EXPERIMENTS .................................................................................................... 21

4.1 Schematic of the System ................................................................................................................... 21

4.2 Analyze the Signal ............................................................................................................................ 23

4.3 Designing the Crystal Oscillator ....................................................................................................... 24

4.4 Conclusion ........................................................................................................................................ 25

CHAPTER 5 WIRELESS TESTING ........................................................................................................ 26

5.1 S-parameters...................................................................................................................................... 26

5.2 Test Fixture of Anechoic Chamber ................................................................................................... 27

5.3 ADS Data Analyzation ...................................................................................................................... 28

CHAPTER 6 CONCLUSION ..................................................................................................................... 30

APPENDIX A PRODUCT SPEC .............................................................................................................. 31

REFERENCES ........................................................................................................................................... 33

1

CHAPTER 1

INTRODUCTION

In this chapter, we set the stage for the work presented in this thesis. This research project focuses

on developing a wireless indoor positioning system that can potentially be commercialized for

civil use. Compared to the outdoor positioning systems that are more fully developed with products

that produce navigation solutions, the lack of existing stable, robust and precise wireless indoor

positioning systems aroused our interest in developing one. Such a system can locate objects or

people inside the building and can be extremely useful for users such as malls, hospitals and fire

departments to provide navigation aid or emergency services.

Among all the methods available, we chose to use the phase difference calculation method.

By figuring out the phase difference between the transmitted and received signal, we are able to

calculate the distance and thus provide the details of location. Our method proved to work with

several wire-connected experiments. In order to visualize and extract the phase difference in a

wireless environment, we connected two antennas to the network analyzer, changed the distance

between the antennas and analyzed the phase of S21 in ADS. Eventually we were able to find the

correct distance by extracting information from the slope of phase.

In Chapter 2, we first describe our goals and then briefly discuss the prior works published

and their connection to our project. Various methods have been announced in developing indoor

positioning systems and tests have been done. We combine two of the methods together from some

of the prior works due to our own specifications. In Chapter 3, we elaborate the system, theory and

equations derived from our project in detail. We report on simulations of our equations using

Matlab and perform some calculations. We discuss the valuable information in the data we

2

gathered. In Chapter 4, we discuss our next experiment – testing the system that we designed with

signal generators, oscilloscope and wires connected from one side of the lab to the other side. In

Chapter 5, we illustrate our setups for the experiment in a wireless environment. And finally, in

Chapter 6, we summarize the results of the project. We compare the percentage of error from

different simulation settings and provide thoughts on future research approaches.

Work on this research is supported by Department of Electrical and Computer Engineering.

3

CHAPTER 2

GOALS, LITERATURE REVIEW AND APPROACHES

In this chapter, we give an overview of several methods described in the prior works that were

published. We will then illustrate the method that was particularly related to our research project.

And we close by a thorough description of the previous work that our system depends on.

2.1 Goals

In this section, we describe the goals, the limitations and potential applications of the project.

Our goals:

Number of Dimensions: 3

Accuracy: +/- 3 cm

Maximum range: 15 m

Frequency range: 850 MHz ~ 950 MHz

Fundamental capabilities:

o 3-D location of each user in an indoor environment

o Graphical display of location information

Limitations:

Small operational area

Low signal power

Only applicable to indoor environment

Potential applications:

Mall and hospitals for location guidance

Firefighters and military

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2.2 TDOA & TOA

In this section, we describe TDOA (time difference of arrival) method. There is a similar method

called TOA (time of arrival) which needs to take multi-path conditions into consideration and we

will discuss it at the end of the section.

One of the basic ways to define the position of an object is to use triangulation: use the

geometric properties of triangles to estimate the location by measuring the objects from different

reference points. The idea of TDOA is to determine the relative position of the transmitter by

examining the difference of time at which the signal arrives at the reference points, rather than the

absolute arrival time [1]. And we can derive the distance from the object to the reference points by

computing the attenuation of the emitted signal strength or by multiplying the radio signal velocity

and the travel time as shown in Figure 2.1 and equation (2.1).

Figure 2.1 Find the location of object using TDOA method. Modified from “Survey of Wireless

Indoor Positioning Techniques and Systems,” H. Liu, H. Darabi, P. Banerjee and J. Liu, 2007

5

For a 2-D location of an object such as point P, we can estimate the hyperbolas from two

or more TDOA measurements from fixed points (A, B, and C) and find their intersections. The

equation of the hyperboloid is given by

222222

, )()()()()()( zzyyxxzzyyxxR jjjiiiji (2.1)

Similarly, TOA is a synchronized version of TDOA where two nodes have a common clock.

The receiver node can determine the TOA of the incoming signal and directly calculate its distance

from the transmitter by multiplying the estimated TOA by the speed of light [2]. By making

measurements at least at three different reference points, we can find the intersection of the defined

circle and unveil the possible position of the object as shown in Figure 2.2.

Figure 2.2 Find the location of object using TOA method

In this case, points A, B and C are the reference points. By drawing out circles centered at

A, B and C with radius d1, d2 and d3 respectively, we can find their intersection at point O, which

is essentially the location of the object.

6

The TOA method is simple and straightforward but it needs to take multipath effects into

consideration. Since the measurements all happen indoors, multipath effect signals are reflected

and attenuated by walls, furniture or other noise or interferences in an indoor environment. Such

effects reduce the accuracy of the signals collected. Another disadvantage of the TOA approach is

that it requires precise time synchronization of all the devices used, which is hard to achieve and

will increase the cost of the system.

In conclusion, although TDOA/TOA is very popular, it does not have high precision and

requires multiple receivers to gather data, so we do not pick the TDOA or TOA method alone in

our project.

2.3 UWB

In this section, we introduce UWB (ultra-wideband) technology. We will analyze the advantages

and disadvantages of this method and its applications.

Ultra-wideband, or UWB, is a radio technology for short-range, high-bandwidth

communication. It has properties of strong multipath resistance and to some extent penetrability

for building materials which can be favorable for indoor distance estimation, localization and

tracking. UWB waves occupy a large frequency bandwidth (>500 MHz) and in order to avoid

interference with other radio services, the Federal Communications Commission (FCC) has

limited the unlicensed usage of UWB to -41.3 dBm/MHz and the frequency band to 3.1 GHz -

10.6 GHz [2]. Figure 2.3 illustrates how UWB coexists with other radio frequency (RF) standards.

7

Figure 2.3 Regulated UWB spectrum. Modified from “Indoor Positioning Technologies,” Dr. R.

Mautz, ETH Zurich, 2012

In an indoor positioning system utilizing UWB technology, UWB works by measuring the

distance and/or angle of a set of fixed reference points to a moving tag. And the set of

measurements is then used to calculate position through multilateration or multiangulation.

Typically, UWB setups include wave generators and receivers which capture the propagated and

scattered wave.

We use UWB to perform TOA/TDOA measurements. UWB positioning system has a lot

of advantages: First of all, because of the wide bandwidth, the UWB signals pass easily through

walls, equipment and clothing (but not metallic and liquid materials) [1]. Secondly, it transmits a

signal over multiple bands of frequencies simultaneously and for a much shorter duration than

those used in conventional RFID. Those short duration signals make it easier to filter in order to

separate the correct signals from the other signals generated from multipath. And finally, the short

duration signals permit an accurate determination of the precise TOA and could achieve very high

indoor location accuracy (20 cm). If we combine the method of TOA and AOA (angle of arrival),

it is possible to determine the location of an object using only two reference points.

According to IPTechnologies [3], in order to detect a moving person using UWB, time-

invariant direct waves and strong static signals reflected from furniture need to be subtracted. With

8

known locations of the emitter and receiver antennas, estimated ranges from the reflected waves

can be used for any range-based algorithm such as TOA/TDOA multilateration to determine the

object position as described above. An example of such a system with only two reference points

is given in Figure 2.4.

Figure 2.4 Passive localization setup. Modified from “Indoor Positioning Technologies,” R.

Mautz, ETH Zurich, 2012

The technology of a UWB system is extremely applicable for designing an indoor

positioning system. However, due to the frequency range requirement, we decided not to move

forward with this technology.

2.4 FMCW

In this section, we describe the theory and applications of FMCW (frequency modulated

continuous wave). Since it is the type of technology that we can easily visualize, design and test

in the lab, our system is heavily dependent on it.

An FMCW radar transmits a continuous carrier modulated by a periodic function such as

a sinusoid or sawtooth wave to provide range data. The difference between FMCW and CW

(continuous wave) radar is that FMCW’s wave is modulated, which adds to the ranging capabilities

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[4]. The theory behind localization using FMCW technology is to put a unique “time stamp” on

the transmitted wave at every instant. By measuring the frequency of the signal that returns, the

time delay between transmission and reception can be measured and therefore the range can be

determined. And to avoid the Doppler effects, the amount of frequency modulation must be

significantly greater than the expected Doppler shift.

Figure 2.5 Sawtooth signal showing the time delay. Modified from “Compact FMCW Radar for

GPS-Denied Navigation and Sense and Avoid,” J. D. Mackie, 2014 [5]

Figure 2.5 is an example of the sawtooth produced using the FMCW method. The blue

trace is the transmitted wave and the red trace is the received wave. From the figure, we can read

off the following parameters:

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fmax: maximum frequency achieved

fmin: minimum frequency achieved

Δf: the difference between transmitted and received frequencies

T: period of sweep from fmin to fmax

Since the round trip time delay Δt is directly related to the frequency differences (Δt = T* Δf*(fmax-

fmin)), we can calculate the distance between the reference point and the object by R=c*Δt/2 [6].

Plugging in the previous equation, we get

)(*2 minmax ff

fcTR

(2.2)

Another way to construct an FMCW system is to compare the phase difference between the

transmitted and received signals. And this is essentially the method we use.

2.5 Previous Work

In this section, we will show an example of the system described by one of the papers published.

This system inspired us in designing our own system for experimentation.

In [1], the authors proposed a system that is similar to a GPS system with three sensors and one

active reflector. The system operates at a frequency of 2.45 GHz. The spatial position of the object

can be calculated by solving a set of triangulation equations and the inaccuracy of the position

measurement is directly related to the precision of the FMCW radar distance measurement [7].

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Figure 2.6 Geometrical arrangement of the 3-D radar tracking system. Modified from “Precise 3-

D Object Position Tracking Using FMCW Radar,” ,M. Vossiek, R. Roskosch and P. Heide, 1999

If we only consider the system of Figure 2.6 in 2-D conditions, with fixed coordinates for

sensor 1 and sensor 2, and d1 and d2 measured from the sensors to the reflectors, the position P of

the reflector can be calculated.

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Figure 2.7 Schematic block diagram of a) the sensor and b) the reflector unit. Modified from

“Precise 3-D Object Position Tracking Using FMCW Radar,” M. Vossiek, R. Roskosch and P.

Heide, 1999

To realize the system in hardware the system depicted in Figure 2.7 was proposed. The system

operates at 2.45 GHz and the VCO (voltage controlled oscillator) of the sensor is fed to the

reference block (composed of a 2.45 GHz SAW (surface acoustic wave) delay line and a mixer)

and a diplexer that is connected to the transmit-receive antenna. The instantaneous phase of the

VCO is measured on the reference path. The diagram on the right is the reflector unit. With a clock

generator, the reflector unit is alternately switched between transmit and receive mode. The

received signal is delayed by the SAW device and will be amplified before being transmitted back

to the sensor unit. The distance between the sensor and the reflector can be calculated by

performing multiple measurements.

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2.6 Conclusion

In the previous sections, we discussed three of the methods that were proposed to solve the problem

of indoor positioning in the papers published. There are also a lot of other methods available, such

as the angle based methods and signal property based methods such as RSSI (received signal

strength indicator), dead reckoning (DR), map matching (MM), as well as fingerprinting methods

using waves such as infrared radiation (IR), radio frequency identification (RFID), GSM (Cell-

ID), Bluetooth, ZigBee, ultrasound, acoustic waves and Wi-Fi [8]. In our project, we chose to

apply the mixed concept of TDOA and FMCW based on the following concerns: First of all,

TDOA is an easy-to-understand method. The time difference between the transmitted and received

signals can be measured with a clock and the distance between reference point and the object can

be calculated using triangulation. Secondly, since our target operating frequency region is between

850 MHz and 950 MHz, it will be a waste to apply UWB technology. TDOA and FMCW are

sufficient for the frequency range that we are interested in. Finally, because we are dealing with

signal transmission in an indoor environment with very short distances, the data that we collect

should be extremely precise to a small unit level. By adding the concept of FMCW to TDOA, we

are guaranteed to achieve higher precision in our data. Based on the concerns above, we designed

our own system and the details will be provided in the next several chapters.

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CHAPTER 3

SYSTEM STRUCTURE AND SIMULATION

In this chapter, we will first discuss the system that we designed, how it works and how it could

be applied to wireless indoor positioning systems. Then we will look deeply into the simulations

that we ran and analyze the results.

3.1 Our System

In this section, we will describe how the system we designed works, and the derivation and

equations that are related to our system.

Figure 3.1 The system we designed

Figure 3.1 shows the system that we designed. The system is composed of two parts: the client

side (left) that is composed of a signal generator, a PMU (phase modulation unit) and an antenna,

and the server side (right) that consists of a mixer, a crystal oscillator and an antenna. The

fundamental principle of the system is simple: the signal generator from the client side will

15

generate a signal and this signal will be transmitted by the antenna (St1). The antenna on the server

side will receive the signal transmitted by the client side (Sr1), mix with the crystal oscillator and

pass through the notch filter (filter out the signal with higher frequency). The filtered signal will

be transmitted by the antenna on the server side (St2). When the antenna from the client side

receives the transmitted signal (Sr2), the PMU will analyze the phase difference between the

received signal and the original signal and compute the distance r. The flow of the signals is

indicated by the arrows shown in Figure 3.1.

Assume the signal produced by signal generator has an amplitude A and phase Ψ0

)2sin()(S 0t1 tfAt c (3.1)

In equation 3.1, fc stands for carrier frequency, which is the initial frequency transmitted by the

signal generator.

The signal received on the server side will have a modified amplitude of B and a time delay of r/c

assuming the speed of light is c.

))(2sin()(S 01 c

rtfBt cr (3.2)

Assuming the signal generated by the crystal oscillator has amplitude C and phase Ψr, we find that

)2sin()( rLOLO tfCtS (3.3)

In equation 3.3, LO means local oscillator. The crystal oscillator is a local oscillator that produces

a frequency at fLO.

To find St2, the signal transmitted by the server, we need to mix Sr1 with SLO:

16

)2sin(*))(2sin()(*S)(S 012 rLOcLOrt tfCc

rtfBtSt

)))2())(2cos(())2())(2cos(((*2

100

rLOcrLOc tf

c

rtftf

c

rtfBC

)*2)(2cos(

)*2)(2cos(

*2

1

0

0

rcLOc

rcLOc

c

rftff

c

rftff

BC

(3.4)

And finally, the signal received at the client side is composed of the original transmitted signal and

the transmitted signal from the server side after mixing with the local oscillator. We also need to

take the time delay into consideration.

212 SSS ttr

)2sin( 0 tfA c

)*2))((2cos(

)*2))((2cos(

*2

1

0

0

rcLOc

rcLOc

c

rf

c

rtff

c

rf

c

rtff

BC

)*)24()(2cos(

)*)24()(2cos(

*2

1)2sin(

0

0

0

rLOcLOc

rLOcLOc

c

c

rfftff

c

rfftff

BCtfA

(3.5)

We define

rLOc

rLOc

c

rfff

c

rfff

f

0

0

0

*)24(3

*)24(2

1

17

c

rffff c *81232 (3.6)

From which we can find

f

cr

8

* (3.7)

From the equation derivation, we are able to calculate the distance r. We believe that with multiple

client devices and server devices available, we are able to expand our solution from 2-D to 3-D

and eventually provide the users with a localization solution.

The core of the project is to find a device, or the PMU unit, that can find, record and

calculate the phase difference. One possible way is to observe the waves on the oscilloscope,

record the data and do hand calculation for the distance. Another way is to use an SDR (software

defined radio). The wave of the original and the received signal can be found on the flow graph

using the software in companion with SDR. After exporting data, we can use computational

software like Matlab or Python to perform postprocessing analysis of the phase information. A

third way is to use the network analyzer and look for the phase of S21. We will discuss each method

in detail in the next few sections and chapters.

3.2 Simulation Using Matlab

In this section we will discuss our initial simulation results using Matlab.

Knowing the equations for all the signals that we want to visualize, we implemented the

equations in Matlab. To simplify the process, we assumed the unknown A in equation 3.1, B in

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equation 3.2 and C in equation 3.3 to be 1. We also assumed a 2 m distance between the reference

(server side) point and the object (client side), so r = 2.

Figure 3.2 Matlab simulation with fC = 900 MHz and fLO = 20 MHz

In Figure 3.2, we set fC to 900 MHz and fLO to 20 MHz. The red trace is the original signal

sent out from the client side (St1) and the blue trace is the final received signal (St2) not taking the

original transmitted signal into consideration. It is obvious that St2 can be defined by some

envelope function because we can visualize a smooth curve outlining its extremes. Because we are

not able to directly visualize phase in this case, we need to figure out an alternative way to do the

calculation. A good method to find the time delay and calculate the distance is to find the nulls of

the envelope and compare the nulls with the original signal. We can find Δt by examining the

difference of nulls at the same instance for St1 and St2. And then we can calculate r using equation

(3.8):

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ctr * (3.8)

3.3 Matlab Accuracy Verification

In this section, we will look deeply into the traces and data generated by Matlab.

For an accurate value of null, we decided to zoom in on Figure 3.2 by setting fC to 50 MHz

and fLO to 5 MHz with the other variables remaining the same (A=B=C=1 and r=2). Note that

although the chosen frequency fC is not within our desired frequency range, the result from the

simulation will help us to prove the correctness of our equation derivation. The frequency of the

local oscillator, on the other hand, can be any arbitrary value as long as such frequency is

achievable in designing the hardware. Simulation yielded the graph in Figure 3.3.

Figure 3.3 Matlab simulation with fC = 50 MHz and fLO = 5 MHz

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Similar to Figure 3.2, in this figure the red trace is St1 and the blue trace is St2 . We can see

clearly that without adding back in the original signal transmitted, there is a shift from the red trace

to the blue trace. This is understandable because from the graph, we found that Δt, the distance

between points A and B, is about 10 ns. Applying equation (3.8), r = Δt*c = 10e-9 * 3e8 =3 m.

The r calculated is very close to the value of r (2 m) that we assumed in the initial setup. This

simulation in Matlab proved that our equation derivation works under low frequency conditions.

But this is the ideal case and can be different from real life scenarios.

For the next step, we tested our systems in the lab with cables connected to the signal

generator and oscilloscope. We added length to the system physically and visualized the response

of the received signal. In the next chapter, we will discuss the wired positioning system.

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CHAPTER 4

WIRED EXPERIMENTS

In this chapter, we will describe the physical testing system that we set up in the lab with wires

connected. We measured the time difference between the transmitted and the received signal on

the oscilloscope and calculated the distance r.

4.1 Schematic of the System

In this section we will show the schematic of the system that we built in the lab and the photo of

the actual testing environment.

Figure 4.1 Schematic of the wires-connected system

Figure 4.1 is the simplified version of the system we designed in Figure 3.1 which makes

it easier for lab testing. Signal Generator 1 (SG1) generates the original signal St1 and the output

of SG1 is connected to a long BNC cable (across the lab) before one branch being fed into the

oscilloscope that is next to Signal Generator 2 (SG2). The term “a” is the length of the cable. The

22

other branch of SG1 is one of the inputs to the mixer while the other input is the signal generated

by SG2, or the signal that we assumed to be from the crystal oscillator. We designed a crystal

oscillator later in the experiment to replace SG2. The output of the mixer is also fed into the

oscilloscope.

Figure 4.2 Lab testing setup

The oscilloscope has four channels. We connected the signal generated from SG1 (signal

generator on the other side of the room not shown in Figure 4.2) to channel 2 and the signal that

comes out of the mixer to channel 1. In this experiment, we ignore the delay from St2 to Sr2 and

directly analyze the time difference between St1 and St2 by applying different distances “a” between

SG1 and the oscilloscope. Setting SG1 to be 200 MHz and SG2 to be 20 MHz, we can visualize

the motion of waves on the oscilloscope as we change “a”. We read off data from the oscilloscope

23

directly, calculated the related distance and compared with the results we got from the Matlab

simulation.

4.2 Analyze the Signal

Figure 4.3 Data from the oscilloscope

In Figure 4.3, the yellow trace is St1 and the green trace St2. The shape of the waves is

similar to Figure 3.3. In this experiment, we first find one of the nulls of the green trace and mark

it with X2, and then we add a section of cable to “a”, find the removed null and mark it with X1.

The length of the added section of cable is known to be 30 cm. From the oscilloscope, we found

that Δt = ΔX = 720 ps. Applying equation (3.8), r = Δt * c = 720e-12 * 3e8 = 0.216 m = 21.6 cm.

The percentage of error is 28%. We tried again with a longer section of cable that has length of 60

cm and the percentage of error dropped to 20.8%.

24

The possible reasons for errors are: (1) Inaccurately marked the nulls on the wave because

of inaccurate eye-measurement. (2) Connection problems. We sent the signal of SG1 from one

side of the room to the other by connecting multiple cables together. The noise coming from the

connections could lead to errors. (3) The effect of waves travelling along the cable might be

different from the waves travelling in a wireless environment. The signal delay caused by the

properties of the cable can also lead to errors.

4.3 Designing the Crystal Oscillator

The crystal oscillator we designed in this experiment is related to one of the labs taught in ECE

453. Figure 4.4 is a Colpitts oscillator designed to operate at 20 MHz.

Figure 4.4 Design of Colpitts oscillator operating at 20 MHz in ADS

25

We first set up the model for simulating the crystal oscillator (the circled part). The

characteristic of the crystal oscillator can be generated by adjusting the values of RLC (resistor,

inductor and capacitor) in series and the value of the capacitor in parallel with RLC. The frequency

response of this part of the circuit peaks at exactly 20 MHz. We then added other elements to set

the bias point of BJT and improved the stability and bandwidth of the circuit following the guide

of the lab and made some adjustments [9]. After the verification process in ADS (Advanced Design

System), we soldered the electronic components on the circuit board and repeated the experiment

we described earlier. There was not a large improvement in percentage of error compared to the

previous experiment.

4.4 Conclusion

With the simplified experiment, we are able to visualize the transmitted and received signals on

the oscilloscope. The signals are similar to the results in our Matlab simulation. As stated in section

2.1, our goal is to achieve an accuracy of two to three centimeters. Both of the experiments

described in this chapter have larger error values than expected. Some of the possible reasons are

inaccurate eye-measurement of time differences and multiple connections of cables. In the next

chapter, we apply a different testing method trying to eliminate errors.

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CHAPTER 5

WIRELESS TESTING

In this chapter, we will discuss a wireless testing environment that we developed and analyze the

experimental results. In the previous chapter we developed the theory of the system, ran the

simulation in software and did experiments with wires connected. But since our goal is to develop

a wireless indoor positioning system, it is crucial for us to setup a wireless testing environment.

5.1 S-parameters

According to Wikipedia, S-parameters, or scattering parameters, describe the electrical behavior

of linear electrical networks when undergoing various steady state stimuli by electrical signals [10].

S-parameters also describe the response of an N-port network to voltage signals at each port [11].

Figure 5.1 S-parameters for a two-port network

In Figure 5.1, a1 and a2 are incident voltage waves and b1 and b2 are the reflected voltage waves.

S11, S12, S21 and S22 are the network’s individual S-parameters.

2221212

2121111

aSaSb

aSaSb

(5.1)

27

From equation (5.1) and assuming that a1=V1+, a2=V2

+, b1=V1- and b2=V2

-, it can be derived that

1

1

1

111

V

V

a

bS is the input port voltage reflection coefficient

2

2

2

222

V

V

a

bS is the output port voltage reflection coefficient

2

1

2

112

V

V

a

bS is the reverse voltage gain

1

2

1

221

V

V

a

bS is the forward voltage gain

In our case, we are interested in S21, the response at port 2 due to a signal at port 1. Connecting

two antennas to the two ports and varying the distance between them, we can visualize the change

in phase of S21. We are able to derive a function of frequency and distance.

5.2 Test Fixture of Anechoic Chamber

Figure 5.2 (a) Schematic of the chamber. (b) The actual chamber.

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We designed and constructed a 24’ by 24’ mini anechoic chamber (shown in Figure 5.2)

for the experiment with one fixed and one movable SMA (SubMiniature version A) connector for

antennas. The antennas are surrounded by the absorbing foam to eliminate the problem of

multipath and to insulate the testing environment from the disturbance outside. The product spec

of the antenna and the absorbing foam can be found in Appendix A.

The shortest distance between the two antennas is 15 cm. During the experiment, we first

calibrated the Network Analyzer. We set the start frequency to be 700 MHz and the stop frequency

to be 1100 MHz with 1601 points. Then we connected the antennas to one side of the SMA

connectors and the other side of the SMA connectors was connected to the two ports of the network

analyzer, and we displayed the phase of S21 on the network analyzer. Then we could observe the

change of slope in the phase of S21 by moving the sliding arm. The markings on the sliding arm

indicate the distance between the two antennas. Finally we saved the data from the network

analyzer for postprocessing.

5.3 ADS Data Analyzation

We ran the experiment for five different distances between the two antennas: 15 cm, 20 cm, 25

cm, 30 cm and 35 cm. After running simulations, we exported the s2p file that contains all the

information about the S-parameters and read the file into ADS, the Advanced Design System. ADS

is a world leading electronic design automation software for RF, microwave, and high speed digital

applications [12]. We used ADS to make accurate plots of the measurements. In this section, we

discuss our findings of phase at 15 cm.

29

Figure 5.3 Reading data of the phase of S21 with 15 cm separation of antennas in ADS

As an example, we will discuss the measurement of phase at a separation of 15 cm. The slope of

the changing phase of S21 indicates the group delay.

GroupDelay (5.2)

In the formula above, is the phase angle while is the frequency. Because we wanted to know

the group delay in seconds and the phase in Figure 5.3 was in degrees while the frequency was in

MHz (106 cycles/second), we need to convert the frequency to degrees/second by multiplying it

with 360 degrees/cycle [13]. We found that

seGroupDelay 80178.1360*10*)0.8148.910(

940.179734.1746

(5.3)

According to equation (3.8), r = GroupDelay*c = 0.1714 m =17.14 cm, which gives us an error

rate of (17.14-15)/17.14*100% ≈ 12.5%. Similarly, we found a lower percentage of error for other

separation distances compared to our experimental results with the oscilloscope.

30

CHAPTER 6

CONCLUSION

In this research project, we first discussed the currently existing methods to solve the problem of

indoor positioning, analyzed their advantages and disadvantages and explained our choice of

method. We then introduced the structure of the system that we designed and the equation

derivation of the system. We also described three different methods to test the accuracy of the

system: Matlab simulation, wire-connected testing and wireless testing. By analyzing data that we

collected from the three methods, we found that finding the phase delay of S12 in the wireless

testing environment gave us the most accurate results with the lowest percentage of error.

For the next step of the project, the principal task is to find or design a device that can

automatically capture the phase of S21 and calculate the phase delay. Although the network

analyzer is extremely convenient to use in the lab, in real life circumstances, the indoor positioning

device should have a small body and light weight. We also need to refine the system described in

Chapter 5 by adding filters, amplifiers and modulators and by expanding the area for experiment.

PCB boards need to be designed for circuit integration. And most importantly, we need to keep

track of the system to make sure that it meets all the specs in our goal described in Chapter 2.

In conclusion, the market requirement of wireless indoor positioning systems in the future

can be seen and the method we proposed in our thesis is proved to work. A lot of future work can

be done to improve the system, including hardware and software designs. Hopefully this kind of

device will bring benefits to those who need it.

31

APPENDIX A. PRODUCT SPEC

A.1 Antenna

The antenna we described in Chapter 5 is from Digikey [14].

Figure A.1 Digikey Antenna

32

A.2 Absorbing foam

The absorbing foam is from PPG Aerospace Cumming Microwave (C-RAM SFC 4) [15].

33

REFERENCES

[1] H. Liu, H. Darabi, P. Banerjee and J. Liu, “Survey of Wireless Indoor Positioning

Techniques and Systems,” IEEE Transactions on System, Man and Cybernetics, Part C:

Applications and Review, vol. 37, no. 6, November 2007

[2] G. Bellusci, “Ultra-Wideband Ranging for Low-Complexity Indoor Positioning

Applications,” PhD thesis, Universita degli Studi di Pisa, 2011. Available at

https://www.xsens.com/wp-content/uploads/2014/01/Thesis_PhD_Giovanni_Bellusci.pdf

[3] R. Mautz, “Indoor Positioning Technologies”, habilitation thesis, ETH Zurich, 2012.

Available at http://e-collection.library.ethz.ch/eserv/eth:5659/eth-5659-01.pdf

[4] T. Otto, “Principle of FMCW Radars.” Available at

http://www.slideshare.net/tobiasotto/principle-of-fmcw-radars

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Avoid,” M.S. thesis, Brigham Young University, 2014. Available at

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[6] “Continuous Wave Radar,” ES310 Introduction to Navel Weapons Engineering course

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[7] M. Vossiek, R. Roskosch, P. Heide, “Precise 3-D object position tracking using FMCW

Radar,” 29th European Microwave Conference, Munich, 1999

34

[8] Z. Farid, R. Nordin, M.Ismail, “Recent Advances in Wireless Indoor Localization

Techniques and System,” Journal of Computer Networks and Communications, vol.

2013, Article ID 185138

[9] S. Franke, “Lab 4 Quartz Crystal Oscillator,” lab manual for ECE 543, Department of

Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2012

[10] “Scattering Parameters”, available at https://en.wikipedia.org/wiki/Scattering_parameters

[11] ”S-parameters,” available at http://www.microwaves101.com/encyclopedias/s-parameters

[12] Advanced Design System (ADS), available at

http://www.keysight.com/en/pc-1297113/advanced-design-system-ads?cc=US&lc=eng

[13] “Group Delay Measurements,” available at

http://www.microwaves101.com/encyclopedias/group-delay-measurements

[14] “Abracon LLC APAMS-118,” available at

http://www.microwaves101.com/encyclopedias/group-delay-measurements

[15] “C_RAM SFC 4 4.3’’ x 24’’x 24’’,” available at

http://stores.cumingmicrowave-online-store.com/c-ram-sfc-4-4-3-x-24-x-24/