A novel model for Synchronization and Positioning by using Neural Networks by ijcsiseditor


									                                                                   (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
           10        -10              5     100                 0                   inf           3.5E6        -9.32        1.76        -49.76       -8.64

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                                                                                                                         ISSN 1947-5500
                                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                             Vol. 9, No. 5, May 2011
   By evaluation of the test results, some points are
  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
    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.

    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

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                                                                                                                     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

              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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                                                                                                            ISSN 1947-5500
                                                                        (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                            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
                                                                                                     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
solving the three equations with one of the different methods                            Conference on , vol.2, pp.899-902, 16-18 Oct. 2010.
such as singular value decomposition, numerical analysis or

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


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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