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Proceedings Paper

Comparison of techniques for target detection in noise
Author(s): Michael E. Parten; K. C. Yong
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Paper Abstract

One of the basic problems in pattern recognition is the detection of a pattern in noise. This problem becomes particularly difficult if the spectral content of the signal and noise overlap. In this case, high levels of noise make detection of the signal very difficult. Noise cancellation using adaptive filters has been successful when the characteristics of the noise source and the signal are known. Another problem in pattern recognition involves recognizing the same pattern in different spatial positions. Some special high order neural networks have been shown to exhibit positional invariance, but these systems do not work well in noisy environments. The combined problem of identifying a target that varies in position and is embedded in noise has been approached by cascading systems that attempt to remove the noise and then detect the target with positionally invariant systems. In this paper, a number of different approaches to detecting a specific, translational target in noise are examined and compared. These techniques include, among others, adaptive filtering and a higher order neural network. The higher order neural network incorporates both translational invariance and noise reduction.

Paper Details

Date Published: 10 June 1994
PDF: 9 pages
Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); doi: 10.1117/12.177755
Show Author Affiliations
Michael E. Parten, Texas Tech Univ. (United States)
K. C. Yong, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 2232:
Signal Processing, Sensor Fusion, and Target Recognition III
Ivan Kadar; Vibeke Libby, Editor(s)

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