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Exact sensitivity analysis for optimization of multi‐coupled cavity filters
Author(s) -
Bandler J. W.,
Chen S. H.,
Daijavad S.
Publication year - 1986
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.4490140104
Subject(s) - filter (signal processing) , control theory (sociology) , sensitivity (control systems) , filter design , prototype filter , mathematics , m derived filter , lossless compression , topology (electrical circuits) , computer science , algorithm , electronic engineering , engineering , control (management) , combinatorics , data compression , artificial intelligence , computer vision
This paper describes an efficient approach to the simulation and exact sensitivity evaluation of multi‐coupled cavity filters. The approach uses sensitivity formulae for such responses as input or output reflectron coefficient, return loss, insertion loss, transducer loss, gain slope and group delay, which are derived for a two‐port equivalent of a general network described by its symmetrical impedance matrix. The formulae are specialized to the case of multi‐coupled cavity filters, using a filter model which takes into account many non‐ideal factors such as losses, frequency dependent coupling parameters and stray couplings. The formulation also treats synchronously or asynchronously tuned structures in a unified manner. Explicit tables of first‐ and second‐order sensitivities w.r.t. all variables of interest, including frequency, are presented. Three problems of significant practical value in manufacturing of multi‐cavity filters are solved with the direct application of our formulae. A 10th‐order filter is considered for all three cases. The first case is simultaneous optimization of the amplitude and delay responses to obtain a self‐equalized filter. The second case is accurate prediction of the responses for a lossy filter by simulating a lossless filter. The third case involves parameter identification of the filter from simulated measurements on its responses.