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Proceedings Paper

Analysis of complex radar data sets using fuzzy adaptive resonance theory map
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Paper Abstract

This paper will evaluate one promising method used to solve one of the main problems in electronic warfare. This problem is the identification of radar signals in a tactical environment. The identification process requires two steps: clustering of collected radar pulse descriptor words and the classification of clustered results. The method described here, Fuzzy Adaptive Resonance Theory Map (Fuzzy ARTMAP) is a self-organizing neural network algorithm. The benefits of this algorithm are that the training process is very stable and fast and that it needs a small number of required initial parameters and it performs very well at novelty detection, which is the classification of unknown radar emitters. This paper will discuss the theory behind the Fuzzy ARTMAP, as well as results of the processing of two `i real^i radar pulse data sets. The first evaluated data set consists of 5242 radar pulse descriptor words from 32 different emitters. The second data set consists of 107850 pulse descriptor words from 112 different emitters. The radar pulse descriptors words that were used by the algorithm for both sets of data were radio frequency (RF) and pulse width (PW). The results of the processing of both of these datasets were better than 90% correct correlation with actual ID, which exceeds the results of processing these datasets with other algorithms such as K-Means and other self-organizing neural networks.

Paper Details

Date Published: 9 August 2004
PDF: 8 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.541302
Show Author Affiliations
Michael J. Thompson, Naval Research Lab. (United States)
John C. Sciortino Jr., Naval Research Lab. (United States)

Published in SPIE Proceedings Vol. 5429:
Signal Processing, Sensor Fusion, and Target Recognition XIII
Ivan Kadar, Editor(s)

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