A novel model for Synchronization and Positioning by using Neural Networks

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 5, May 2011

A Novel Model for Synchronization and Positioning
by using Neural Networks

Hossein Ghayoumi Zadeh*, Siamak Janianpour and Javad Haddadnia
Department of Electrical Engineering
Sabzevar Tarbiat Moallem University
Sabzevar, Khorasan Razavi, Iran
Email: h.ghayoumizadeh@gmail.com*

Abstract— In this paper by using a Low Noise Amplifier (LNA), a used in global positioning system (GPS), recently. Contrary to
synchronization and positioning system is designed. Parameters other methods, this system will not affect the normal operation
that cause the system to be far from ideal condition such as of the satellites, because the time delay is calculated passively.
S-Parameters, Noise Figure, IIP3, and Gain are considered that Also there is no need to carry extra hardware in spaceships and
is one of the advantages of this system. In this stage this process is this will reduce the cost of this procedure.
a little slow so by adding the neural network to the system the
speed of synchronization is increased. By using the neural By calculating the received uplink signal TDOA using three
network the time needed to calculate the time difference of or four satellite on earth orbit, the position of the transmitters
arrival (TDOA) is significantly decreases. can be localized. When the transmitter is located on the earth,
three satellites and when the elevation of the transmitter is not
Keywords-component; Global Positioning System, Low-noise known, four satellites are needed to localize the position of the
amplifiers, Neural networks. transmitters. So in the passive systems, approximation of the
delay time between the signals receive time from two different
I. INTRODUCTION sensors plays a significant role in measurement of the distance
and direction of the transmitters. When a signal is emitted into
The TDOA approximation has so many different
the environment from transmitter, it spreads with a specific
applications such as communication, electronic war and
speed, so two receivers with different distance from the
medical engineering. Following some of these applications will
transmitter, sense the signal with a time delay. If S(t) is the
be discussed. One of the most important applications of TDOA
emitted signal from transmitter and assume that there is just
is in positioning of the transmitters. Nowadays radar and sonar
one way for signal transmission, then the signals first will
systems are widely used with many different military or
receive to the nearest receiver and with a delay to the next
nonmilitary applications and their importance in security
receiver, the delay time is shown with D. So the goal is to
problems are so that they are parts of the strategic system of
measure this delay and approximation of this delay has a
each country so to protect the radars; the usage of the passive
significant role in synchronization and positioning process [2].
radar is become popular, increasingly[1]. Although in these
type of radars the basic principle of the radars are dominant but
the transmitter of the radar is omitted from the system and by II. SYNCHRONIZATION
omitting the transmitter the receiver will become hidden from The controll system is shown in fig. 1. In this circuit the
the sight of the enemies. The place of transmitter is one of the LNA model is used in S-parameters block. In this block the
most important parameters that assign the duties of the radar. values of S-parameters, Noise Figure and IIP3 are changeable.
Another application of TDOA is in measurement and
controlling the coolant current of the atomic reactors. Also it In this circuit the Gaussian Signal is used as input
can be used to localize the position of brain that controls the (Fig. 2). Also the white noise is applied to the signal and
simultaneity of the activities in epileptic patients. Further it is considered as a non ideal factor.

Figure 1. Controlling system designed for Synchronization

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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 5, May 2011

Figure 2. The Gaussian Signal that is applied to the control
circuit as input

The structure of the synchronization is so that the input
delay of T1 is considered by the integer delay block. After the
amplification stage, the changes of the S-parameter are exerted
to the input signal that simulates the LNA stage, and it is
transferred to the output. The second input in this simulation
that is also the Gaussian signal, is multiply to the first signal
with a delay and it is transferred to the output. The point is that
the second output also has delay equal to T2 that is shown in Figure 3. LNA circuit that is used in the controlling circuit.
the “integer delay 2” block. Now by using a feedback from the
output to the second input, the delay is changeable so that the 3-dB frequency of 5.7 GHz. The IIP3 is about -3 dBm and the
both inputs become concurrent. noise figure (NF) ranges from 3.06-3.8 dB over the band of
The easiest and most effective way for synchronization of interest. Input reflection coefficient S11 is below -8.79dB for
the both input signals is to multiply the feedback output with the design. The LNA consumes 5.77 mW from a low supply
the first input, and simultaneously check the output until the voltage of 1.8 V. A figure of merit is devised to compare the
output becomes maximal. Now the delay can be recorded and proposed designs to recently published wideband CMOS
stored. When the both inputs become synchronize the output LNAs. The proposed topology achieves a lower NF than that of
will become maximal. the topology capacitive cross-coupling with inductors, with the
additional advantage of removing the bulky inductors. It is
shown that the LNA is designed without on-chip inductors that
III. LOW NOISE AMPLIFIER (LNA) its performance is comparable with inductor-based designs.
In this paper the model of an inductorless low-noise The LNA circuit that is used in this system is shown in fig. 3.
amplifier (LNA) is used. [3] This LNA is designed for ultra- LNA properties such as gain, IIP3 and etc. that are used for
wideband (UWB) receivers and for microwave access, synchronization in this system are given in TABLE I.
covering the frequency range from 0.4 to 5.7 GHz using 0.18-
μm CMOS technology. Simulation results show that the Also an example of the test results is shown in
voltage gain reaches a peak of 19.6 dB in-band with an upper TABLE II.

TABLE I. LNA PROPERTIES THAT ARE USED IN SIMULATION

Technology BW(GHz) NF (dB) S11 (dB) Gain (db) IIP3 (Dbm) Power(mW) No.of Coils FOM

0.18μm 0.4 ~ 5.7 3.06 ~ 3.8 <-8.79 19.66 -3 5.77 0 3.68

TABLE II. TEST RESULTS

Output Gain1 Delay Gain Noise Figure (db) OIP3(dbm) Frequency S11 S12 S21 S22
-8
10 -10 5 100 0 inf 3.5E6 -9.32 1.76 -49.76 -8.64

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 5, May 2011
By evaluation of the test results, some points are
achievable:
1. The delay time is respectively related to noise, noise
figure, initial delay and OIP3. The LNA gain will affect
the delay time but it has a little impact. Also the impact
of S-parameter is so small that it is neglect able.
2. By increasing the noise figure the delay time will
increase. Also in high value of the LNA gain its
increase will cause an increase in delay time, but noise
figure does not show such a behavior.
3. The effect of the OIP3 in comparison with S-parameter
is higher. (by changing the S-parameter values not a big
change is observed.
4. By increasing the OIP3 value the delay time is
decreased.
But the problem in designing such a controlling system is Figure 5. The initial signal that is used to train the neural network
that about 45 second is needed to process such a big amount of
data and it is one of the disadvantages of this system, because
moreover to input delay time we will lose the time needed for
the calculation process of software that is very bad for
synchronization systems. So to reduce the calculation time the
neural network is used.

IV. USAGE OF NEURAL NETWORK IN SYNCHRONIZATION
In this system a linear neural network is used. To minimize
the errors in these networks, the training process is done by
using squares least mean algorithm (Fig. 4).
The neural network that is used in this process must be so
fast and so accurate. First of all the goal is to synchronize the
signals with each other by using the neural network. To train
the neural network the previous Gaussian signal is used that is
shown in fig. 5.
The objective function of the neural network can be Figure 6. The objective signal that is used to train the system.
achieved from multiplying of two Gaussian signals that is
shown in fig. 6. An example of the signal that is used for test purpose is
Now many samples of Gaussian signal with different delay shown in fig. 7. The noises and delays are obviously shown in
time are applied to the neural network for training purpose. For this figure.
instance to test the designed neural network the signal that is
shown in fig. 7 is used. Also to test the robustness of the
network, samples that contain noise and have different delays
in comparison with original signals are used.

Figure 4. The structure of linear neural network used in this control system. Figure 7. The test signal that contains noise and delay

50 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 5, May 2011
Now the results achieved by using neural networks are In this method the speed of delay calculation in
shown in fig. 8. The neural network tries to find the appropriate significantly increased and the calculation time is decreased. It
delay time that leads to the objective function by using the is good to mention that the Gaussian signal enters the system
iterative methods. If you look at the fig. 8 carefully you will see periodically and the system must be able to sweep the input
that some parts of the chart are drawn thicker and or bolded. continuously. To consider the noise in this system, for
That is caused by increasing the accuracy of the neural network synchronization other techniques must be added to this system.
and the number of iterations. In other words, in this method Now we are going to describe these techniques. Assume a
two signals are studied by different random delays and the signal like the signal in fig. 10 is given to the system as input.
delay that cause the output to reach the objective function is First an intact period of the signal must be given to the system
recorded. as the training sample.
In the proposed method the noise in the Gaussian signal,
has a small effect on calculation of the delay time that is clearly V. USAGE OF NEURAL NETWORK IN POSITIONING
shown in fig. 8. There are so many methods for positioning systems based
By decreasing the accuracy of the network the segregation on the calculation of the signal time-of-flight. One of these
of the signals with different delay time are shown more clearly methods is TDOA. In this method to calculate the position of
(fig. 9). the TAG, the distance between the TAG and a node is
calculated and it is compared with the distance of the TAG
with another node. In this method the TAGs are just the
transmitter and the nodes are just receiver. In 2 dimensional
systems 4 TAGs and in 3 dimensional systems 5 TAGs are
used.

Figure 8. The output of the neural network.

Figure 10. Three period of signal with consideration the effect of noise.

Figure 9. The more detailed chart of the output signal. Figure 11. Geometrical structure of a 3D TDOA.

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Vol. 9, No. 5, May 2011
In this study the unknown position of the TAG is shown etc. In this study the numerical analysis is used. TDOA is
with function E (Equation 1). mostly used in open areas for personal uses, air traffic control
(ATC) or military systems.
 x,y,z  A sample of positioning process is shown in fig. 12.
Because of linearity of the equations, theoretically there
And the receivers are defined as (Equation 2): isn’t any error in this process but the practical problem, is the
signal transfer time calculation from the transmitter to the
P0, P1, ..., Pm, ..., PN.Pm = (xm, ym, zm), 0 ≤ m ≤ N (2) receiver. In this paper the role of clock pulse generation and
synchronization is significant. To increase the accuracy of the
Where N is the function dimension. system, the clock pulse must be synchronized in every node. If
the generated clock pulse was in the order of 1ns, the best
The distance between the each transmitter and the receiver achievable accuracy in the positioning system is about 30
is defined as Rm and R0 is the distance between transmitter centimeter that means we can find the position of the
and the false origin (it is assumed that one of the receivers is transmitter with maximum error of 30 centimeter.
located at the false origin). As it is shown in fig. 11 the
resultant time is calculated as follows (Equation 3):

  m  Tm  T0  

In this equation,  m is the time that the signal needs to
arrive to the mth receiver. The delay time that is considered in
this stage must refer to the calculated time in synchronization
stage. Now, the time duration (τm) can be calculated by using
correlation function Pm  P0  .

Substituting R0 instead of T0 in above equation and after
some rearrangements, finally we will have a line equation with
constant coefficients (equation 4)[4,5].

 Am x  Bm x  Cm x  Dm x  0  

That Am, Bm, Cm and Dm in equation 4 are defined as
follows.
Figure 12. Positioning with TDOA method

 2xm 2 x1 (4a)
Am   REFERENCES
 m  1
[1] Fujiwara, R.; Mizugaki, K.; Nakagawa, T.; Maeda, D.; Miyazaki, M.; ,
"TOA/TDOA hybrid relative positioning system using UWB-IR," Radio
and Wireless Symposium, 2009. RWS '09. IEEE , pp.679-682, 18-22
2ym 2 y1 (4b)
Jan. 2009.
Bm   [2] Dawei, L.; , "Application of assisted TDOA positioning technology in
 m  1 vehicle positioning and navigation," Mobile Technology, Applications
and Systems, 2005 2nd International Conference on , pp.5 pp.-5, 15-17
Nov. 2005.
[3] Ali Shirzad Nilsaz, Mohsen Khani Parashkoh, Hossain ghauomy-zadeh,
2z m 2 z1 (4c) Zhuo, Zou, Majid Baghaei-Nejad, Li-Rong Zheng. “low power .18um
Cm  
 m  1 cmos ultra wideband inductor-less lna design for uwb receiver”.Asia
Pacific Conference on Circuits and Systems,2010.
[4] Kian Meng Tan; Choi Look Law; , "GPS and UWB Integration for
indoor positioning," Information, Communications & Signal Processing,
2007 6th International Conference on , pp.1-5, 10-13 Dec. 2007.
xm  z m  z m
2 2 2
x12  z12  z12 (4d)
Dm   m   1   [5] Rabinowitz, M.; Spilker, J.J., Jr.; , "A new positioning system using
 m  1 television synchronization signals," Broadcasting, IEEE Transactions on
, vol.51, no.1, pp. 51- 61, March 2005.
[6] Wenjuan Jia; Chunlan Yang; Guocheng Zhong; Mengying Zhou; Shuicai
It is very easy to calculate the three unknown parameters Wu; , "Fetal ECG extraction based on adaptive linear neural network,"
(x,y,z) that are the coordinates position of the transmitter, by Biomedical Engineering and Informatics (BMEI), 2010 3rd International
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such as singular value decomposition, numerical analysis or

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Vol. 9, No. 5, May 2011

AUTHORS PROFILE

Hossein Ghayoumi Zadeh received the B.Sc. Javad Haddadnia received his B.Sc. and M.Sc.
degree in electrical engineering with honors from degrees in electrical and electronic engineering
Sabzevar Tarbiat Moallem University, Sabzevar, with the first rank from Amirkabir University of
Iran, in 2008. He is now M.Sc. student in electrical Technology, Tehran, Iran, in 1993 and 1995,
and electronic engineering at Sabzevar Tarbiat respectively. He received his Ph.D. degree in
Moallem University in Iran. His current research electrical engineering from Amirkabir University
interests include computer vision, pattern of Technology, Tehran, Iran in 2002. He joined
recognition, image processing, artificial neural Tarbiat Moallem University of Sabzevar in Iran
network, intelligent systems, fuzzy logic and soft since 2002 as an associated professor. His research
computing and etc. interests include neural network, digital image
processing, computer vision and medical
Engineering. He has published several papers in
these areas. He has served as a Visiting Research
Scholar at the University of Windsor, Canada
during 2001- 2002. He is a member of SPIE,
Siamak Janianpour received the B.Sc. degree in CIPPR, and IEICE.
mechanical engineering with honors from the
Islamic Azad University Tehran south branch,
Tehran, Iran, in 2008. He is now M.Sc. student in
electrical and electronic engineering at Sabzevar
Tarbiat Moallem University in Iran. His current
research interests are computer vision, pattern
recognition, digital image processing and analysis,
intelligent systems, intelligent healthcare systems
and etc. He is a member of the IEEE.

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