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Updated: Jun 9, 2026

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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
Published on: February 4, 2018
Summary
We compare synthetic discriminant function filters and minimum noise and correlation energy filters. A new definition for multicriteria optimal trade-off correlation filters is introduced based on this comparison.
Area of Science:
- Optical engineering
- Signal processing
- Pattern recognition
Background:
- Synthetic discriminant function (SDF) filters are widely used in pattern recognition.
- Minimum noise and correlation energy (MNCE) filters offer an alternative approach.
- Objective quantitative comparison is needed to understand their performance trade-offs.
Purpose of the Study:
- To quantitatively compare the optimal trade-off approach for SDF filters with the MNCE filter.
- To introduce a new general definition for multicriteria optimal trade-off correlation filters.
Main Methods:
- Utilized an objective quantitative figure of merit for filter performance evaluation.
- Compared the performance of SDF filters and MNCE filters based on defined criteria.
Main Results:
- The study provides a quantitative assessment of the performance differences between SDF and MNCE filters.
- A novel general definition for multicriteria optimal trade-off correlation filters was established.
Conclusions:
- The comparison offers insights into the strengths and weaknesses of each filter type.
- The new definition facilitates the development of more advanced correlation filters.
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