Minimum mean square error filter for pattern recognition with spatially disjoint signal and scene noise

Philippe Refregier; Bahram Javidi; Guanshen Zhang; Farokh Parchekani

doi:10.1117/12.163619

9 November 1993 Minimum mean square error filter for pattern recognition with spatially disjoint signal and scene noise

Philippe Refregier, Bahram Javidi, Guanshen Zhang, Farokh Parchekani

Proceedings Volume 2026, Photonics for Processors, Neural Networks, and Memories; (1993) https://doi.org/10.1117/12.163619
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

A minimum mean square error filter for pattern recognition problems with input scene noise that is spatially disjoint (or nonoverlapping) with the target is described. The filter is designed to locate the target by producing a delta function output at the target position. The filter minimizes the mean square of the difference between the desired output delta function and the filter output in response to a noisy input data. We show that the filter output has a well defined peak and small sidelobes in the presence of spatially disjoint target and scene noise.

Citation Download Citation

Philippe Refregier, Bahram Javidi, Guanshen Zhang, and Farokh Parchekani "Minimum mean square error filter for pattern recognition with spatially disjoint signal and scene noise", Proc. SPIE 2026, Photonics for Processors, Neural Networks, and Memories, (9 November 1993); https://doi.org/10.1117/12.163619

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