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9 August 2004 Analysis of complex radar data sets using fuzzy adaptive resonance theory map
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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.
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Michael J. Thompson and John C. Sciortino Jr. "Analysis of complex radar data sets using fuzzy adaptive resonance theory map", Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); https://doi.org/10.1117/12.541302
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