Figure 5
: Common aircraft RCS Nopriansyah et.al, 2008.
Aircraft speed that is presented at Radar screen can be obtained by using Doppler principle that is shown
in Equation 3. Figure 3 illustrates how to calculate object speed by using Doppler principle.
2
d
.v f
cos θ
λ =
3 which f
d
is Doppler shift, v is aircraft speed, λ is
wavelength, and θ is angle between direction of
incoming signal propagation with direction of antenna movement.
In this paper we use RCS and speed data taken from previous research done by Nopriansyah et.al,
2008 as presented in Table 1.
Table
1: List of aircraft RCS and speed data Nopriansyah et.al, 2008.
No. Aircraft Type RCS
Speed kmhour
1. Bell 47G
3 168.532
2. F-16 Fighting Falcon
5 1470
3. Hawk200 8
1,000.08 4. Su-30
Sukhoi 15
2,878.75 5. Cobra
AH-1S 18
227.796 6. Cassa
C-212 27
364.844 7. CN-235
30 459.296
8. A-310 Airbus
100 980
19
2.6. A Brief Introduction to Information Fusion
In general, information fusion is a technique in combining physical or non-physical information form
from diverse sources to become single comprehensive information to be used as a basis for prediction or
estimation of a phenomenon. The prediction or estimation is then used as the basis for performing
decisions or actions. Figure 6 illustrates the concept of information fusion.
Figure 6
: The concept of information fusion Ahmad Sumari, 2008.
The information sources can be from as follows: • observation data from distributed sensors,
• commands and data from operator or user, • a priori data from an existing database.
Referring to Hall, 2001 in Ahmad Sumari, 2008, for obtaining a comprehensive information in
decision level, we can select many technique options such as Boolean operator methods AND, OR or
heuristics value such as M-of-N, maximum vote, or weighted sum from hard decision and Bayes method,
Dempster-Shafer, and fuzzy variable for soft decision. In this paper we use the Boolean operator for all
approaches.
3. A GENERIC MODEL OF NEURAL
NETWORK AIRCRAFT IDENTIFICATION SYSTEM
The NN-AIS generic model is modified from Sumari et. al 2008b which consists of three
processing blocks namely Pre-processing Block, Aircraft Identification Block, and Post-processing
Block.
3.1. NN-AIS Diagram Block
Figure 7:
Generic NN-AIS architecture.
Because the system processes two different data, so there will be two NNs within the system, one is for
processing aircraft RCS data and the other is for processing aircraft speed data. The generic
architecture of NN-AIS is depicted in Figure 7.
In general, the Pre-processing Block prepares the inputs in form of vector patterns to the two NNs. The
Aircraft Identification Block performs identification of the received inputs to the knowledge stored in the
NNs. The Post-processing Block fuses the output resulted from Aircraft Identification Block, converts
and displays the estimated identity of the received inputs.
The peculiar feature of an NN is if it already learned the received input, it will produce an exact
result. But if it has never learnt the received input, it will try to find the best match or estimated result.
3.2. Training in Supervised NN-AIS Darusman
et.al., 2009
In the NN1 architecture we use 150 neurons in hidden layer, while in the NN2 architecture we use 20
neurons. We take these numbers after carrying out some observations to find the most appropriate
numbers. For training the two NNs, we did some researches to find the most appropriate activation
functions to be utilized to the NN architectures. We select the combination of logsig and purelin activation
functions for hidden layer and output layer.
The BPN needs some time to train its structure in order to learn the vector patterns given to it by
minimizing the difference error between the net’s outputs with the target’s values. In order to train the
supervised BPN, we created vector patterns as presented in Table 2 and Table 3. The results of the
NNs training are depicted in Figure 8 and Figure 9.
Table 2 : Aircraft speed as inputs to NN1 and
the NN1 learning’s targets.
No. Aircraft Speed
Target
1. 168.532 00000001
2. 1470 00000010
3. 1,000.08 00000100
4. 2,878.75 00001000
5. 227.796 00010000
6. 364.844 00100000
7. 459.296 01000000
8. 980 10000000
20
Table 3
: Aircraft speed as inputs to NN2 and the NN2 learning’s targets.
No. Aircraft RCS
Target
1. 3 00000001
2. 5 00000010
3. 8 00000100
4. 15 00001000
5. 18 00010000
6. 27 00100000
7. 30 01000000
8. 100 10000000
Figure 8
: NN1 knowledge for aircraft speed data.
Figure 9:
NN2 knowledge for aircraft RCS data.
3.3. Training in Unsupervised NN-AIS Utama
