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Advances in Fuzzy Systems
Volume 2012 (2012), Article ID 406204, 10 pages
Research Article

Comparison of Detection and Classification Algorithms Using Boolean and Fuzzy Techniques

1Department of Engineering and Computer Engineering, Wayne State University, Detroit, MI 48202, USA
2Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA

Received 11 February 2012; Accepted 7 September 2012

Academic Editor: Ashu M. G. Solo

Copyright © 2012 Rahul Dixit and Harpreet Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Modern military ranging, tracking, and classification systems are capable of generating large quantities of data. Conventional “brute-force” computational techniques, even with Moore’s law for processors, present a prohibitive computational challenge, and often, the system either fails to “lock onto” a target of interest within the available duty cycle, or the data stream is simply discarded because the system runs out of processing power or time. In searching for high-fidelity convergence, researchers have experimented with various reduction techniques, often using logic diagrams to make inferences from related signal data. Conventional Boolean and fuzzy logic systems generate a very large number of rules, which often are difficult to handle due to limitations in the processors. Published research has shown that reasonable approximations of the target are preferred over incomplete computations. This paper gives a figure of merit for comparing various logic analysis methods and presents results for a hypothetical target classification scenario. Novel multiquantization Boolean approaches also reduce the complexity of these multivariate analyses, making it possible to better use the available data to approximate target classification. This paper shows how such preprocessing can reasonably preserve result confidence and compares the results between Boolean, multi-quantization Boolean, and fuzzy techniques.