On Linear Mapping of Filter Characteristic to Position of Tuning Elements in Filter Tuning Algorithm

Abstract

Abstract-This work presents a novel approach in building a multidimensional approximator which is used as a linear operator for mapping the vector of detuned filter characteristic to the vector of deviations of tuning elements. This has been done for the purpose of using it in postproduction filter tuning algorithm. With the use of collected sets of deviations of tuning elements and filter characteristics corresponding to them, the least squares method (LSM) is applied to determine the matrix which realizes the linear mapping between these vectors. The matrix found in this method approximates the vectors of both spaces (filter characteristics and corresponding deviations of tuning elements). In tuning process this matrix is used to determine the vector of tuning element deviations for a given detuned filter characteristic read from Vector Network Analyzer. To increase the "quality" of linear operator filter characteristics are transformed with the use of Karhunen-Loeve transform (Principal Component Analysis). In contrast to non-linear artificial intelligence approximators used in filter tuning and published to-date, this method does not require a time-consuming training process. Filter tuning experiments have been performed and proved the correctness of the presented approach.

Filter Tuning Based on Linear Decomposition of Scattering Characteristics

“…To model scattering characteristic s the only pieces of information that must be collected are these that stem from detuning a filter on a single element only while the others are set to their proper positions. It would allow us to reduce significantly the number of {∆z, s} pairs which need to be collected comparing to methods presented in [4][5][6][7].…”

“…On the other hand it can deal with many types of filters including very complex ones as shown in a tuning experiment, Section 5. An important advantage is a reduction of time needed to prepare a model for new filter type compared to methods [4][5][6][7]. A comparison of mentioned methods is depicted in Figure 1.…”

“…This mapping is performed with the use of artificial neural network (ANN) multidimensional approximator [4,5], neurofuzzy system [6] or linear matrix operator [7]. Especially [4,5] methods based on ANN are very well examined and proved to be very fast and able to deal with any type of filter (high complexity, many crosscouplings).…”

“…Similarly as in methods [4][5][6][7], method [8] considers direct correspondence between filter characteristics and tuning elements. Elaboration [8] is motivated by the fact that in methods [4,6,7] an inconvenient process of controlled, random filter detuning must be done for each filter type to train, e.g., Artificial Neural Network. That process takes a long time because it makes it necessary to collect a lot of random {∆z, s} pairs containing tuning element deviations ∆z and corresponding scattering parameters s. In [8] an approximator A: s→ ∆z is proposed, which requires a very coarse set of {∆z, s} pairs.…”

A Fast Tuning Method for Microwave Filter Using Vf-Asm Technology

“…For a de-tuned filter, the corresponding characteristic filter parameters can be extracted from S-parameter measurement through many techniques, such as Cauchy method [4][5][6]. Recently a great deal of effort has been made on computer aided tuning for microwave filter [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In this paper, a novel hybrid method are proposed.…”