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Proceedings Paper

Clustering with unsupervised neural networks with applications to data fusion
Author(s): Stelios C.A. Thomopoulos; Chin-Der D. Wann
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Paper Abstract

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.

Paper Details

Date Published: 1 February 1994
PDF: 12 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172541
Show Author Affiliations
Stelios C.A. Thomopoulos, The Pennsylvania State Univ. (United States)
Chin-Der D. Wann, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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