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The Performance of Supervised and Unsupervised Neural Networks in Performing Aircraft Identification Tasks
Arwin Datumaya Wahyudi Sumari
1
Aciek Ida Wuryandari
2
Maman Darusman
2
Nur Ichsan Utama
2
1 Departemen Elektronika, Akademi Angkatan Udara Jl. Laksda Adisutjipto, Yogyakarta 55002 – INDONESIA
Telp. 0274 486922 ext 6101 Fax. 0274 488918 Email: arwin91aauyahoo.co.id
2 Sekolah Teknik Elektro dan Informatika - ITB, Kampus ITB Labtek VIII Lantai 2, Jl. Ganesa 10, Bandung 40132 – INDONESIA
Telp. 022 2502260 Fax. 022 2534222 Email: acieklskk.ee.itb.ac.id
, maman_darusmanyahoo.com
, nur_ichsanymail.com
ABSTRACT
This paper is a report on our research progress in the area of aircraft identification by utilizing neural networks and information fusion. In this paper we address the performance comparison of supervised and unsupervised
neural networks in aircraft identification tasks in a generic system called Neural Network-based Aircraft Identification System NN-AIS. We select Adaptive Resonance Theory ART for the unsupervised neural
network and Back Propagation Network BPN for the supervised one. As for previous research, we use two kinds of input namely aircraft Radar Cross Section RCS and average speed. Their performance will be
validated by using already-learnt and never-learnt patterns
.
Keywords
: ART, BPN, aircraft identification, RCS, speed
1. INTRODUCTION
Aircraft identification task is a critical matter to recognize the identity of an aircraft that is entering a
monitored air space. The sooner the observed aircraft is identified, the faster the authorized authority can
make a decision. In normal flight procedure, all aircraft flight plans must be reported to the authorized
authority to be recorded. The records will be used to monitor every single aircraft movement in the
monitored air space.
The reported flight plans ease the authority to track and identify a certain aircraft that is displayed on
monitor room’s displays. A problem is arisen when the authority cannot identify a certain aircraft that is
detected by radar system. There are two possibilities when an aircraft cannot be identified. First, there is a
possibility the aircraft transponder for answering the interrogation signal from ground station is not
working properly or failed. Second, there is an intention to turn-off the transponder in order to hide
the aircraft identity.
For the second reason, we can conclude that the aircraft must be in undercover missions and can be a
threat to our country’s sovereignty. Because of it, the authority must have a way to identify the unidentified
aircraft before something harmful occurs in the future. One of ways in identifying aircraft is by using its
Radar Cross Section RCS value and combining it with its speed. On the other hand, one approach that
has been known well for object recognition is neural networks.
In this paper we address the utilization of two kinds of neural network architecture for performing
aircraft identification tasks namely supervised and unsupervised. We use the two types of input for
training and validating the networks and measure their performance. In general our proposed system is called
as Neural Network-based Aircraft Identification System NN-AIS.
The structure of the paper is as follows. Section 1 cover the background of the paper and it will be
followed by Section 2 which covers a short introduction to neural networks as well as related
matters to aircraft RCS and speed. Section 3 presents the NN-AIS design as well as the NN training. Section
4 delivers the system validation. The paper is summarized in Section 5 with some concluding
remarks.
2. A SHORT INTRODUCTION TO NEURAL
