Application of neural networks to the detection of a target in noise
Michael E. Parten, Mustafa Ulutaş, Jon P. Davis · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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 signal-to-noise ratios make detection of the signal very difficult. Noise cancellation using adaptive filters has been successful when the noise source is 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 can be approached by cascading systems that attempt to remove the noise and then detect the target with positionally invariant systems.