Table 5 : Aircraft speed data and its associated pattern
No.
Aircraft Type Average
speed kmh
Vector Pattern
1.
Bell 47G 158.532
000010011111
2.
F-16 Fighting Falcon 1,470
010110111110
3.
Hawk 200 1000.08
001111101000
4.
Su-30 Sukhoi 2,878.75
101100111111
5.
Cobra AH-1S 227.796
000011100100
6.
Cassa C-212 364.844
000101101101
7.
CN-235 PT DI 459.296
000111001011
8.
A-310 Airbus 980
001111010100
21
The results of the unsupervised NNs training are depicted in Figure 10 and Figure 11.
Figure 10
: Unsupervised NN1 knowledge in form of top- down and bottom-up weights for speed pattern.
Figure 11 : Unsupervised NN2 knowledge in form of top-
down and bottom-up weights for RCS pattern.
4. NN-AIS VALIDATION
In this section we present the NN-AIS validation on the two types of NNs we already explained.
4.1. Supervised NN-AIS
To validate the unsupervised NN-based system, we select three aircrafts in random manner, namely Bell
47G as presented in Table 6. In this validation we modify the speed and RCS inputs to see if the system
works as it is designed. The results are presented Table 7 and the identification process is depicted in
Figure 12.
Table 6 : Validation data for supervised NNs.
Input Data 1
st
Detection 2
nd
Dectection 3
rd
Detection
Speed 168 169
167 RCS 3
3.2 2.7
Table 7
: Validation result.
Output Types Aircraft
Type Number of
Information
Speed Bell 47G
3 RCS Bell
47G 3
Final result Bell 47G
Figure 12: The identification process carried out by the
supervised NNs.
4.2. Unsupervised NN-AIS
To validate the unsupervised NN-based system, we select three aircrafts in random manner, namely
Cobra AH-1S and Bell 47G. In this validation we set up the vigilance parameter
ρ = 0.5. The results are
presented in Figure 13and Figure 14.
Figure 13
: Identification process result for Cobra AH-1S.
Figure 14
: Identification process result for Bell 47G.
4.3. Measuring the Performance
4.3.1. Supervised NN-AIS
As we can see from the validation presented in Table 5, the NNs in supervised NN-AIS tries to
22
recognize the detected aircraft’s patterns by generalizing
the knowledge of what they “see” and what they have already learnt during training session.
The result is the system is able to produce the correct aircraft estimation, namely Bell 47G helicopter.
4.3.2. Unsupervised NN-AIS
Figure 14 and Figure 15 clearly present the mechanism carried out by unsupervised NN-AIS in
identifying the detected aircrafts. The identification is done directly by matching the detected aircrafts’
patterns with the knowledge they already memorized during the training session. The result is the system is
able to produce the correct aircraft estimation, namely Bell 47G
and Cobra AH-1S helicopters. 5.
CONCLUDING REMARKS
We have presented the utilization of supervised BPN and unsupervised ART NNs and observe their
performances in identifying the identity of aircrafts in NN-AIS framework. The two approaches result in
good estimations even though the vector patterns have been modified. The supervised NNs use the
generalization capability to recognize the patterns while the unsupervised ones use matching mechanism
in recognizing the patterns.
By noticing the results of this research, there is a possibility that these approaches can be combined
with the real-life Radar system in order to increase its identification tasks. The NNs-based system can give
significant advantage especially to identify harmful unlisted detected aircrafts.
REFERENCES
[1] A.I. Wuryandari, A.D.W. Sumari, and Nopriansyah, Aircraft Identification by Using
Combination of Neural Network and Information Fusion, to be appeared in Jurnal Penelitian dan
Pengembangan Telekomunikasi JURTEL, No.
2, Vol. 13, Desember 2008, ITTelkom, Bandung [2] A.S. Ahmad, and A.D.W. Sumari, Multi-Agent
Information Inferencing Fusion in Integrated Information System
, Seri “Information Science and Computing”, Sekolah Teknik Elektro dan
Informatika, Institut Teknologi Bandung, Penerbit ITB, 2008, ISBN 978-979-1344-31-9.
[3] A.D.W. Sumari, et.al., Application of Adaptive Resonance Theory 1 for Identification Friend,
Foe, or Neutral System, Proceedings of the 4
th
International Conference Information Communication Technology and System 2008
ICTS2008, Institut Teknologi 10 Nopember Surabaya, Surabaya, 5 August 2008a, pp. 602-
609, ISSN 1858-1633. [4] A.D.W. Sumari, A.S. Ahmad, A.I. Wuryandari,
and Nopriansyah, Object Identification by Using Combination of Neural Network and Information
Fusion, Proceedings of the 1
st
International Graduate Conference on Engineering and
Science 2008 IGCES2008, Universiti
Teknologi Malaysia, Johor, Malaysia, D31, 23- 24 December 2008b, ISSN 1823-3287.
[5] D.L. Hall and J. Llinas, Eds., Handbook of Multisensor Data Fusion
, USA: CRC Press LLC, 2001.
[6] D.M. Skapura, Artificial Neural Networks: Algorithms, Applications, and Programming,
Addison-Wesley, 1991. [7] D. Priyanto, A.D.W. Sumari, and E.P.T.
Wibowo, Design of Neural Network-based Intelligent Classroom System: A Preliminary
Research, Proceedings of the 1
st
Makassar International Conference on Electrical
Engineering and Informatics 2008 MICEEI2008, Universitas Hasanuddin, 13-14
Nopember 2008, Makassar, pp. 67-72, ISBN 978-979-18765-0-6.
[8] L. Fausset, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications
,
Prentince-Hall, USA, 1994.
[9] M. Darusman, A.D.W. Sumari, and A.I. Wuryandari, Desain dan Implementasi Sistem
Identifikasi Pesawat Terbang Berbasis Jaringan Syaraf Tiruan Model Back Propagation Network,
Prosiding Seminar Nasional Teknologi Informasi dan Aplikasinya 2009
SENTIA09, Politeknik Negeri Malang, Malang, 12 March 2009, pp.
F55-F60, ISSN 977-208-5234-00-7. [10] Nopriansyah, A.D.W. Sumari, A.I. Wuryandari,
and Andaruna, Radar Identification Friend, Foe, or Neutral System using Aircraft’s Radar Cross
Section and Speed based on Adaptive Resonance Theory 1 Artificial Neural Network and
Information Fusion, Proceedings of 2008 National Radar Seminar
, ISSN 1979-2921, April
30, Jakarta, 81-86 in Indonesian. [11] N.I. Utama, A.D.W. Sumari,
