Clustering with unsupervised neural networks with applications to data fusion

Stelios C. Thomopoulos*, Chin Der Wann

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


In this paper, an unsupervised learning artificial neural network, Dignet, is used to design a data fusion for moving target indication (MTI). Dignet is a self-organizing neural network model with simple architecture. The system parameters of Dignet are analytically determined from the self-organization during the learning process. It exhibits fast and stable learning performance. Based on its excellent clustering performance on the statistical pattern recognition, Dignet is used in the design of a multi- sensor data fusion. The data fusion is designed to supplement the decision making of an MTI radar system for multi-target detection. The radar system consists of three different sensors, which receive signals with carrier frequencies located in different bands. The received signals are processed by using digital signal processing techniques, fast Fourier Transform and pulse compression. Features of the received data are extracted from the signal processing stage. The features are then presented to Dignet for data clustering. The well centers and well depths generated by Dignet are then propagated to the fusion center. Another Dignet is used in the data fusion center for second stage clustering. The clusters of patterns created in the second stage clustering are fused by decision making algorithms to make in integrated decision. It is shown that the data fusion for MTI successfully detects and keeps track of multiple moving targets which are embedded in clutter or noisy environments.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsJames L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C.A. Thomopoulos, al et al
PublisherPubl by Society of Photo-Optical Instrumentation Engineers
Number of pages12
ISBN (Print)0819413666
StatePublished - 1994
Externally publishedYes
EventSubstance Identification Analytics - Innsbruck, Austria
Duration: 04 10 199308 10 1993

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceSubstance Identification Analytics
CityInnsbruck, Austria


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