FOURTH QUARTER 2009 1531-636X/09/$26.00©2009 IEEE IEEE CIRCUITS AND SYSTEMS MAGAZINE 43

Feature

Digital Object Identifier 10.1109/MCAS.2009.934706

Implementation of a Complete GPS Receiver Using Simulink

Gihan Hamza, Abdelhaliem Zekry, and Ibrahim Motawie

AbstractDuring the past few years a lot of efforts have been exerted to make the inner working of the GPS receiver visible, clear and easy to learn and modify either on the level of software or hardware. This article adds a step on the route toward the implementation of a more visible, clearer, and easier to learn and modify a single frequency GPS receiver using the C/A code on the L1 carrier. Simulink was used in the implementation of such receiver, thereby introducing a new look for the SDR technology that can be ac-complished via a graphical user inter-face environment.

I. Introduction

During the past decade a lot of efforts have been exerted not only

to open the inner working of the GPS receiver but also to facilitate

the education of the design and implementation of such system.

The Software-Defined Radio technique was used as a tool in the imple-

mentation. From the efforts that were exerted in this field are:

The open source GPS project that was initiated in 1995 and leaded by ■

Clifford Killy and Douglas Baker (with collaboration with others) [1].

This project had an educational aims to help any GPS enthusiast to

learn deeply the internal working of the GPS receiver. They developed

both a commercial hardware and software that constitute a complete

GPS receiver. The hardware was introduced by developing two chip-

sets called GP1010 and GP1020. The GP1010 was used as a front-end

and performs the acquisition phase; while The GP1020 was the track-

ing and navigation data extraction chip that has 6 correlators chan-

nels [2][3]. The software of this project was a C program written in

Borland C. This program contained a library of the GPS functions such

as the satellite location by using the almanac, the ephemeris, comput-

ing the navigation solution and decoding the navigation message.

Dick Benson, a consulting application engineer at Mathworks: de- ■

veloped an incomplete single channel GPS receiver using Simulink.

He implemented acquisition, partial tracking, and no pseudorange

in SIMULINK [4].

The efforts of Kai Bore and Dennis Akos (with collaboration with ■

others) fruited a book titled “A Software-Defi ned GPS and GALILEO

© COMSTOCK

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44 IEEE CIRCUITS AND SYSTEMS MAGAZINE FOURTH QUARTER 2009

Receiver A Single-Frequency Approach” [5]. They

introduced the implementation of a complete

8-channels single frequency GPS receiver using

the C/A code on the L1 carrier using Matlab as the

coding language. This book is accompanied with

39 m-fi les that represent the receiver algorithm

and a record for a real GPS signal collected from

a specially designed ASIC-based front-end.

In general, the algorithm of a complete GPS receiver

is considered complicated and long, because it contains

different stages that deals with the processing of RF, IF,

and baseband signals. Previously, to write either a part

or a complete GPS algorithm you must be professional

in C/C11 programming, assembly, or Matlab. These are

the programming languages that researchers used in

the simulation of the GPS receivers. To write a complete

algorithm for a GPS receiver; a teamwork is needed. Ev-

ery person writes the algorithm of a certain part and

delivers the output to the next person to write the next

part of the algorithm. Using a GUI environment instead

of the hand written programming languages was a far

idea due to the algorithm’s complexity and length.

In This article the 39 m-files that are accompanied

with the book titled “A Software-Defined GPS and GALI-

LEO Receiver: A Single-Frequency Approach” are con-

verted to only 5 Simulink models, after modifying the

algorithm of some parts to conform with the GUI envi-

ronment and make the simulation time faster.

This article adds a step on the route toward a clear,

easy, and transparent simulation of GPS receivers imple-

mented with the SDR technology. The implementation of

a complete 8-channel GPS receiver is accomplished via a

graphical environment, which is Simulink. In general, the

graphical environment makes the relation between the sys-

tem modeling, simulation, and implementation clearer and

easier to debug, because we can put test points anywhere

and obtain immediate results. Also, using a graphical pro-

gramming language eliminates the need to a team work

and enables only one person to finish the simulation of the

complete system in a relatively short time. In general, the

success of using a graphical programming language such

as SIMULINK in simulating a complicated system such as

the GPS receiver will open the route for other complicated

systems to be simulated by the same way. This will facili-

tate the education, the modification and the debugging of

many of the complicated electronic systems.

II. Building The Different Stages Of GPS Receiver

A. The Front-End

The Simulink models of acquisition, tracking, pseudo-

range, and position solution are tested and developed

by a real GPS signal that was collected from the SE4110

ASIC-based front end. Its functional block diagram is

shown in reference [6]. The same signal is used in test-

ing and developing the Matlab algorithms. The main

parameters of these data are:-

Sampling frequency: 38.192 MHz.

IF: 9.548 MHz.

Four-bit samples.

B. Representing the Data Collected from the

Front- End in the Different Domains

The digital samples that were collected from the front-

end can be represented in the dif-

ferent domains using the different

types of scopes available in Simu-

link. Fig. 1 shows the Simulink

model for data representation. In

this figure the first branch gives

the time domain representation

of 1,000 samples that is shown

in Fig. 2. The signal is a noise

like and this is the nature of the

CDMA signal. The second branch

gives a histogram of 1,048,576

samples that is shown in Fig. 3. It

is clear that the 4-bit samples are

translated to 16 levels. The third

branch gives the frequency do-

main representation of 1,048,576 Figure 1. Simulink model to represent the collected data in the different domains.

Read Binary File

Submatrix

Data Type

Conversion

Double

HistogramUser

FFT

User

Vector

Scope 1

Spectrum

Scope

Vector

Scope

Time Domain

Representation

of 1,000 Samples

Frequency Domain

Representation

of 1,048,576 Samples

Histogram

of 1,048,576 Samples

Read Binary File

Submatrix

Data Type

Conversion

Double

HistogramUser

FFT

User

Vector

Scope 1

Vector

Scope

vipmen.bin

Gihan Hamza is a research assistant in the National Institute of Standards, Guiza, Egypt (Tel: +202-26356263; E-mail: gihan_hamza@yahoo.com).

Abdelhaliem Zekry is a professor in the Faculty of Engineering, Ain Shams University, Cairo, Egypt (Tel: +202-24840051; E-mail: aaazekry@asunet.shams.edu.

eg). Ibrahim Motawie is a professor in the National Institute of Standards, Guiza, Egypt (Tel: +202-35715755; E-mail: motawie@nis.sci.eg).

FOURTH QUARTER 2009 IEEE CIRCUITS AND SYSTEMS MAGAZINE 45

samples that is shown in Fig. 4. It is clear that the sym-

metry is around the IF frequency.

C. GPS Receiver Architecture

The operation of a single channel GPS receiver is based

on the structure shown in Fig. 5. In acquisition we deter-

mine one of the visible satellites and determine both the

carrier frequency and code phase roughly. In tracking

we track any changes to these values to ensure correct

data decoding. After that both the pseudorange and po-

sition are calculated. Each block in Fig. 5 is translated

into a Simulink model, as shown in Fig. 6. In Fig. 6 there

are two additional models; one provides the demodu-

lation carrier and the other provides the dispreading

code. Both the demodulation and dispreading models

must be simulated first to feed lookup tables existed in

the acquisition model. These tables contain all the pos-

sibilities of the demodulating carrier and dispreading

codes according to a specified sampling frequency that

is a modifiable value reserved in the Workspace.

Figure 2. Time domain representation of 1,000 samples.

6

4

2

0

–2

–4

–6

–80 0.5 1 1.5 2 2.5

× 105Time

Am

plit

ude

Figure 3. Histogram of 1,048,576 samples receiver.

16

14

12

10

8

6

4

2

0 2 4 6Bins

Num

ber

Within

Bin

8 10 12 14 16

× 104

Figure 4. Frequency domain representation of 1,048,576 samples.

40

20

0

–20

–40

PS

D M

agnitude (

dB

)

–60

0 2 4 6 8Frequency (MHz)Frame

10 12 14 16 18

Figure 5. Structure of a single channel GPS receiver.

Acquisition

Tracking

Pseudorange

and Position Calculation

Incoming Signal

X, Y, Z dt

Figure 6. SIMULINK implementation of a single channel GPS receiver.

Demodulation

Carrier.mdl

Acquisition.mdl

Incoming Signal

Dispreading Code.mdl

Tracking.mdl

Pseudorangeand

Position Calculation.mdl

X, Y, Z dt

46 IEEE CIRCUITS AND SYSTEMS MAGAZINE FOURTH QUARTER 2009

D. Acquisition

Acquisition is a two dimensional search process that

results in identifying the code phase with an uncertain-

ty equals to 6½ – chip and the IF with an uncertainty

equals to 6½ the Doppler search bin size. Acquisition

can be implemented by either of 3-standard methods,

which are: the serial search acquisition, the parallel fre-

quency space search acquisition, and the parallel code

phase space search acquisition. It is customary to use

the first two methods in the traditional implementation

of the acquisition phase, which is implemented on an

ASIC. Implementing acquisition according to the SDR

technology is accomplished according to the third

method [7]. Acquisition was implemented with the par-

allel code phase search technique shown in Fig. 7 [5].

In this technique we search through all the possible fre-

quency bins and parallelize the code phase search such

that the total number of searches per satellite equals

the number of frequency bins. The number of frequen-

cy bins in our case was 29 frequency bins for 14 KHz

search band stepped by a 0.5 KHz frequency interval.

In the Simulink model of acquisition the 29 frequency

bins are searched at the same time where the incoming

signal is multiplied by a look up table containing all the

possible IF frequencies. This means that the total num-

ber of searches for the 32 satellites is 32. Fig. 8 shows

the actual implementation of this

block diagram in Simulink where

it is shown the position of both

the demodulation and dispread-

ing models that were mentioned

before in Fig. 6.

The simulation time of the

acquisition phase that was simu-

lated as m-files was about 183

second; while the time for it when

simulated as a Simulink model

was about 200 seconds. The in-

crease in simulation time comes

from that Simulink is optimized to make FFT and IFFT

not a DFT and IDFT, while Matlab able to do both. So, the

DFT and IDFT were built as a Level-2 M-file S-Function

blocks that made them consume larger time than if they

were implemented in Matlab.

The implementation of the Fourier Transform thus af-

fects the simulation time. It is well known that the FFT

and IFFT are faster than the DFT and IDFT but they affect

only sequences that have a radix-2 length. To use the FFT

and IFFT we have to reduce the number of samples per

C/A code from 38192 to the lower radix-2 value, which

is 32768 5 214. This means that we have to resample the

incoming signal by a frequency of 32.768 MHz. SIMULINK

doesn’t have a block that down sample by a fractional

factor. So, we have either of two ways; the first is to use

the “resample” m-function using the Embedded MATLAB

Function in the SIMULINK model to resample by the fac-

tor (32768/38192).

Using a new sampling rate of 32.768 MHz makes the de-

tected code phase be referenced to the new number of

samples per C/A code, which are 32768 samples. To make

the detected code phase to be referenced again to the old

number of samples per C/A code we multiplied the detect-

ed one by an inverse of the factor that was used in sam-

pling down the frequency. This means that each detected

code phase is multiplied by the factor (38192/32768).

The second way is by inter-

polating the input signal by FIR

interpolation filter to increase

the number of samples per 1 ms

(time of a complete C/A code)

from 38192 to 65536 samples [8].

This signal is then punctured

regularly to reach 32768 samples

per C/A code [9]. Resampling by

this way made the detected code

phase referenced to the new num-

ber of samples per C/A code. So,

a mapping block was created to

eliminate the effect of resampling

Figure 7. Parallel code phase search block diagram.

Incoming

Signal×

× ×DFT

90DFT

IDFT |u|2

Local Oscillator PRN Generator

Fine Resolution

Frequency Search Carrier

FrequencyConj

Figure 8. Acquisition.mdl (Implementation of the Parallel code phase search in SIMULINK).

Incoming

Signal×

× ×DFT

90DFT

IDFT |u|2

Demodulation

Carrier.mdl

Dispreading

code.mdl

Fine Resolution

Frequency Search.mdl Carrier

Frequency

Code Phase and

Peak Metric

Conj

FOURTH QUARTER 2009 IEEE CIRCUITS AND SYSTEMS MAGAZINE 47

on the detected code phases. In the mapping block we

apply the reverse process of interpolation and punc-

turing to the detected code phase. In other words, we

obtain code phases that are referenced to the original

number of samples per C/A code, which is 38192, after

the resampling process and consequently we don’t need

to make any changes to the Tracking model.

Both the demodulating carrier and the dispread-

ing code are resampled according to the new sampling

frequency and then FFT and IFFT of a length of 215 are

used instead of the DFT and IDFT. The resolution of the

detected frequencies will not change because both the

sampling frequency and the Fourier transform length

are changed. The resolution of the detected frequency

is calculated according to the following equation:

Df5fs /2N/2

, (1)

where fs is the sampling frequency and N is the Fourier

transform length.

Using FFT instead of DFT made the simulation time

become about 70 seconds. The Fine Resolution Fre-

quency Search block consumes the largest time from

the overall simulation time. So, after building it as an

S-function, the acquisition simulation time became

about 58 seconds, which amounts only to 1/3 of that of

the previous implementation.

Fig. 9 shows the final results obtained from the acqui-

sition model before resampling. The first branch shows

the PRN of the visible satellites. The second branch il-

lustrates that the peakMetric of all the visible satellites

exceeds 2.5, which is a criteria putted to differentiate

between the visible and nonvisible satellite. The third

branch shows the corresponding carrier frequency of

each visible satellite. The fourth branch gives the cor-

responding code phases of the detected ones. Fig. 10

shows the acquisition results after resampling with the

results sorted from the strongest to weakest visible sat-

ellites. These results are stored in the Workspace as in-

puts to the tracking. Comparing the results of Fig. 9 and

Fig. 10, we see that the resampling process has a minor

effect on the acquisition process in spite of reducing the

acquisition time to only its 1/3 of its original value. Fig.

11 and Fig. 12 show the acquisition results for one of the

visible satellites and invisible satellites respectively. It

is clear that the visible satellite has a single dominant

peak while the nonvisible satellite doesn’t have a dis-

tinct peak such that the ratio of the first to second peak

is more than 2.5.

E. Tracking

The main purpose of tracking is to refine the acquisition

results, track any changes that occur to these values,

and demodulate the incoming signal to obtain the 50 Hz

navigation data bits.

Figure 9. Results obtained from the Acquisition.mdl before resampling.

[1]

[1]

[1]

[1]

21 22 15 18 26

Display

Display 1

Display 2

Display 3

6

13.26 12.09 9.4 4.326 4.21614.62

9547399.0249634

13.404 6288 36321 207xxx

9549657.2341919 9549875.7705688 9548200.3250

Detected

PRN

Double [1 × 8]

Double [1 × 8]

Double [1 × 8]

Double [1 × 8]

Peak

Metric

Carr Freq

Code

Phase

Detected

PRN

Peak

Metric

Carr Freq

Code

Phase

Rearranging The Detected

Sats Descendingly

48 IEEE CIRCUITS AND SYSTEMS MAGAZINE FOURTH QUARTER 2009

Tracking consists of a carrier tracking loop imple-

mented as a PLL and a code phase tracking loop imple-

mented as a DLL. The two loops are combined in one

loop to decrease the number of multipliers that con-

sume a lot of time. Fig. 13 shows the combined code and

carrier tracking loops, i.e., a complete tracking channel

[5]. The implementation of these loops in Simulink is

shown in Fig. 14, where it consists of 7 blocks. From left,

the first block is the feedback block of the DLL. The sec-

ond block represent the incoming signal, where we read

a variable data size, determined by the feedback, from

the file that was recorded by the front-end module. The

third block represents the NCO carrier generator and

the PLL feedback. The NCO carrier generator provides

Figure 12. Acquisition plot for a not visible satellite.

12

10

8

6

4

204

32

10 0 5 10

15 2025

30

× 107

× 104

32

100 0 5 10

15 2025

× 107

104

Figure 10. Results obtained from the Acquisition.mdl after resampling.

Detected

PRN

Detected

PRN

Double [8 × 1]

Double [8 × 1]

Double [8 × 1]

Double [8 × 1]

Double [32 × 1]

Double [32 × 1]

Double [32 × 1]

Double [32 × 1]

Peak MetricPeak Metric

Carr Freq

Code Phase

Carr Freq

Detected

PRN

Peak Metric

Carr Freq

Code Phase

Rearranging the Detected

Sats Descendingly

for {...}

Quired Code Phase

or Iterator

26

9

6

3

21

22

15

18

4.421

3.372

3.006

2.938

12.56

12.32

12.15

10.09

26826

4696

28202

34211

13404

6288

36321

20725

9545015.625

9550843.75

9544312.5

9549914.0625

9547429.6875

9549695.3125

9549921.875

9548250

Display

Display 1

Display 2

Display 3

Figure 11. Acquisition plot for a visible satellite.

12

10

8

6

4

204

32

10 0 5

1015 20 25

30

× 107

× 104

FOURTH QUARTER 2009 IEEE CIRCUITS AND SYSTEMS MAGAZINE 49

a refined value for the carrier fre-

quency, the fourth block is the

PRN code generator which pro-

vide the early, late, and prompt

codes. The fifth block is the inte-

grate block at which we integrate

over the number of samples per

C/A code for the early, late, and

prompt codes. This block con-

sists of summing blocks. The

sixth block is the code loop dis-

criminator, which helps in adjust-

ing the code phase. This discrim-

inator was built as a noncoherent

discriminator according to the

following equation:

codeError51 I E

2 1QE2 22 1 I L

21QL2 2

1 I E2 1QE

2 21 1 I L21QL

2 2 , (2)

where IE and IL are the in-phase

outputs of the early and late codes

respectively. QE and QL are the outputs of the quadra-

ture early and late codes respectively.

The seventh and last block is the carrier loop dis-

criminator and loop filter. The carrier loop discrimi-

nator is built as an arctan discriminator. This type of

discriminators is precise but consumes more time.

This discriminator is built according to the follow -

ing equation:

f5 tan21aQ

Ib , (3)

here f is the phase error. I and Q are the in-phase and

quadrature signals of Costas loop.

The comparison between Fig. 13 and Fig. 14 demon-

strates the resemblance between the tracking block di-

agram and its implementation in Simulink. This means

Figure 13. Combined code and carrier tracking loops (complete tracking channel).

Integrate

and Dump

Integrate

and Dump

Data

Incoming

Signal

Integrate

and Dump

Integrate

and Dump

Integrate

and Dump

NCO Carrier

GeneratorCarrier Loop

FilterCarrier Loop

Discriminator

Integrate

and Dump

Code Loop

DiscriminatorPRN CodeGenerator

90°

l

lE

lP

lL

QL

QP

QE

Q

E

E

P

P

L

L

Figure 14. Top view for the Trackink.mdl (the implementation of the tracking loops in Simulink).

fid

Incoming Signal

FeedBackBlockacqFreq

PRN Code

Generator

carrSin

carrCos

carrFreq_N

tcode_P

blkSize

remPhase_N

acqFreq

R

caCode

iBaseband

Signal

qaseband

Signal

uT

uT

carrError

carrFreq

Reset

I_P

Q_P

acqFreq

Code LoopDiscriminator

codeNco

codeFreq

ResetI_EQ_EI_LQ_L

IEQE

ILQLIP

QP

Integrate Resetting

the I/P Is the Dump

IEIPILQEILQL

×

tcode_P

promptCode

lateCode

earlyCode

caCode

PhaseStep

RemPhase

abSample

IncomingSignal

blksize

fid

remPhase

PhaseStep

chNo

Out4

For Iterator

> 37000 Compare To Constant

ForIterator

1 : N 1

4

remPhase

R

U

remPhase_N

PhaseStep

××××××

×fid3

2

Out21

2

3

NCO CarrierGenerator

Carrier

Loop

Discriminator

and Loop

Filter

50 IEEE CIRCUITS AND SYSTEMS MAGAZINE FOURTH QUARTER 2009

easier building, debugging, and modification in SIMU-

LINK environment. Fig. 15 shows a part of the naviga-

tion data extracted from one of the receiver channels.

The simulation time for the tracking algorithm that im-

plemented as m-files (tracking.m) for a single channel was

about 20 minutes while the time for that implemented as a

Figure 17. The block diagram according to which the Pseudorange and position calculation was built in SIMULINK.

Tracking Results Find PreambleSubframe Start

Calculate Pseudorange

Ephemeris

Ephemeris

TOW

Satellite Position

Sat. Position

Sat. Clock

Correction

Pseudorange

Least Square

PositionXYZ, dt

Figure 18. The final results from the Pseudorange_and_position.

Double

Double

Double

pseudoranges

Double [1x8]

Double

Double

Double

[1x8]

[1x8]

[1x8]

[1x4]

[1x8]

[1x4]

[1x8]

[1x4]

[1x4]

[1x4]

pos(1)X

pos(1)Y

pos(1)Z

U Y

U Y

U Y

E

N

U

Display 3

Display 4

Display 5

Display 2

Display 1

21285293.33907

–1288156.1168178 –4720789.6023873 4079720.7646892 519510.63323641

72.558332232667 46.486710475713 57.26070306979 72.483443421818

4429017.6790361

477646.66344317

1403.372632145

22688941.376222

Display

satClkCorr corrected_psuedoranges

navSolutions.channel.correctedP

Coordinate Conversion (cart2geo - findUtmZone - cart2utm)

Succeeded

pos

Figure 15. Part of the navigation data extracted from the Trackink.

–1.51,0009008007006005004003002001000

–1

–0.5

0

0.5

1

1.5x 104

Figure 16. Flow diagram for the pseudorange and position calculation.

Tracking Result

Calculate Pseudorange

Satellite Position and Clock Offset

Least Square Filter (az, el, DOP, xyz dt)

FOURTH QUARTER 2009 IEEE CIRCUITS AND SYSTEMS MAGAZINE 51

SIMULINK model (tracking.mdl)in Simulink was about 37

minutes. The increase in time comes from that the perfor-

mance of the Product blocks in Simulink is not the same as

in Matlab 7.1.

F. Pseudorange and Position Calculation

This phase includes decoding the 50 Hz navigation bits

(message) according to ICD-GPS-200 (1991) to obtain

the pseudorange, the receiver position, and the receiver

clock offset.

A complete navigation message consists of 25 frames/

pages and each frame consists of 5 subframes. Each sub-

frame contains 10 words and each of them has a length

of 30 bits. Subframes 1, 2, and 3 are the same in all the

frames while subframes 4 and 5 are varying from frame

to other. The subframe consumes 6 s as a transmission

time which means that we need 12.5 minutes to receive

a complete navigation message. In our work we decoded

a complete page that has a time of 30 s.

Fig. 16 shows a flow diagram for calculating the pseu-

dorange, the satellite position and clock offset, and the

receiver position. Fig. 17 shows the block diagram ac-

cording to which the SIMULINK model was built. The

first step is to find the subframe start and then begin

decoding the received data bits. By identifying the sub-

frame start we calculate the pseudorange, the ephemer-

is, and the Time Of Week (TOW). After that we calculate

the satellite clock correction and satellite position. The

final step is to calculate the receiver position and the

receiver clock offset. Fig. 18 shows the results obtained

from that model. The first display shows the corrected

pseudorage values for all the visible satellites, the sec-

ond, third, and fourth displays illustrate the coordinate

conversion from the Cartesian to the UTM system.

III. Conclusion

In general, system design becomes easier and clearer if it is

accomplished via a graphical user interface environment.

The simulated model has an educational and commercial

aims. The educational aims come from the transparency

in the design and simulation of any part of the algorithm.

Also, it is very easy to modify, debug, or test any part of

the system. The commercial aims are embedded in the

tools available in Simulink, such as the RTW embedded

coder [10], [11].

Gihan Hamza received her BSc in Com-

munications and Electronics from Ain

Shams University, Egypt in 1998. She

received the MSc degree from the same

university in 2004 in the field of auto-

mating the long-term measurements.

Now, she is a research assistant in the

National Institute for Standards (NIS) in the Time and Fre-

quency Department and a PhD student. She is interested

in the time and frequency dissemination through using

GPS receivers.

Abdelhaliem Zekry graduated from

Cairo University Egypt in 1969. He was

offered the MSc degree in 1973 from

the same university. He worked as a

scientific coworker at TU Berlin, where

he got his PhD at 1981. He worked as an

assistant prof. at Ain Shams University

(ASU), Egypt 1982. He moved to King Soud University

at 1988 and stayed there for 6 years, where he became

a professor of Electronics. Now he is a professor of

Electronics at the faculty of Egypt, ASU.

Dr. Zekry made intensive research on semiconductor

materials, devices, and circuits. He published more than

70 papers in specialized conferences and periodicals in

addition to two books in Electronics. Now, he is driving

research on electronics for communication especially

the implementation of advanced communication stan-

dards using DSP platforms.

Dr. Zekry has been awarded several prizes for out-

standing research as well as the Decoration of distinc-

tion from the Egyptian President. He has been an IEEE

Member since 1991.

Ibrahim Motawie graduated from Cairo

University, Egypt. He was offered the

MSc degree from Ain Shams University,

Egypt. He got his PhD in 1979 from Paul

Seharie University, France. He worked

as an associate professor at Constantine

University, Algeria for about 6 years.

Now, he is a professor in the National Institute for Stan-

dards (NIS). He is interested in the Time and Frequency

measurements and applications. He published more than

20 papers in specialized conferences and periodicals.

References[1] Available: http://home.earthlink.net/~cwkelley/

[2] Available: http://www.zarlink.com/zarlink/hs/82_GP2015.htm

[3] Available: http://www.zarlink.com/zarlink/hs/82_GP2021.htm

[4] Available: www.xilinx.com/publications/magazines/dsp_01/xc_pdf/

p50-53_dsp-gps.pdf

[5] K. Borre and D. Akos, A Software-Defined GPS and GALILEO Receiv-

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