Automated filter tuning using generalized low-pass prototype networks and gradient-based parameter extraction

A novel technique for automated filter tuning is introduced. The filter to be tuned is represented by a generalized filter low-pass prototype model rather than a specialized equivalent network. The prototype model is based on the minimum number of characteristic filter parameters to represent the fi...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on microwave theory and techniques 2001-12, Vol.49 (12), p.2532-2538
Main Authors: Harscher, P., Vahldieck, R., Amari, S.
Format: Article
Language:English
Subjects:
Assembly
Automated
Circuit simulation
Electromagnetic modeling
Equivalence
Mathematical models
Microwave filters
Microwaves
Military computing
Networks
Optimization
Optimization methods
Parameter estimation
Parameter extraction
Production
Prototypes
Tuning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel technique for automated filter tuning is introduced. The filter to be tuned is represented by a generalized filter low-pass prototype model rather than a specialized equivalent network. The prototype model is based on the minimum number of characteristic filter parameters to represent the filter transfer function correctly. The parameter values are found from a gradient-based parameter-extraction process using measured S-parameters. Automated filter tuning is performed as a two-step procedure. First, the parameter sensitivities with respect to the tuning elements are determined by a series of S-parameter measurements. Second, the parameter values of the filter are compared to the values of the ideal filter prototype found from a filter synthesis, thus yielding the optimal screw positions. This novel tuning technique has been tested successfully with direct coupled three-resonator and cross-coupled four- and six-resonator filters.
ISSN:0018-9480
1557-9670
DOI:10.1109/22.971646