A hybrid tracking algorithm for characteristic mode analysis

Ludick, D. J.; Tonder, Johann van; Jakobus, U.

doi:10.1109/iceaa.2014.6903896

Cited by 21 publications

(20 citation statements)

References 9 publications

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“…For most applications, this technique has been found to yield accurate tracking of individual CMs. However, both this technique and the more evolved hybrid model [6] can break down under three situations: The primary one is described by differences in the fine features of the eigencurrents (over frequency) within one mode, which can decorrelate the currents enough to give the illusion of one mode ending and a new mode beginning; The second is that the mathematical computation of CMs for a symmetric structure can result in multiple eigencurrents with mirror symmetry across the structure (i.e., with flipped currents) even though the characteristic patterns are nearly identical. This causes the tracking algorithm to separate a single mode into two modes at different frequencies; the third is the use of large frequency step size between the adjacent frequency points, which can also lead to tracking errors.…”

Section: Characteristic Mode Trackingmentioning

confidence: 99%

“…The simplest tracking method is based on correlating the eigenvalues across frequency and sorting the modes in ascending order from the lowest frequency to the highest frequency [4]. This approach to tracking and classifying eigenvalues produces errors with symmetric geometries [3] and large frequency step sizes [5], [6]. In contrast, tracking eigenvalues through eigenvectors, or eigencurrents, is a widely adopted technique in antennacentric CM research [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

“…This approach to tracking and classifying eigenvalues produces errors with symmetric geometries [3] and large frequency step sizes [5], [6]. In contrast, tracking eigenvalues through eigenvectors, or eigencurrents, is a widely adopted technique in antennacentric CM research [3]- [6]. Although there are many types of current-based CM tracking, the modal assurance criterion method (MACM) is the most popular one [3].…”

Section: Introductionmentioning

confidence: 99%

“…MACM can be adapted in different ways to offer solutions to some but not all of the problems which have been associated with MACM tracking, e.g. [6]. Even though some of the adaptations to MACM allow for the tracking of modes which spontaneously begin or end, here on referred to as degenerate modes, MACM may not be suitable for use with complex structures or large frequency steps.…”

Section: Introductionmentioning

confidence: 99%

“…The only commercially available software offering CM analysis, FEKO [7], implements a bounded linear correlation function that improves upon MACM. In [6], this specialized linear correlation function is extended by adding extrapolation of the eigenvalues to create a hybrid tracking algorithm that outperforms the previous outlined approaches.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Wideband Characteristic Mode Tracking Utilizing Far-Field Patterns

Miers

Lau

2015

Antennas Wirel. Propag. Lett.

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Abstract-The Theory of Characteristic Modes provides a convenient tool for designing multi-antennas for MIMO applications, as it enables orthogonal radiation patterns to be excited in a given antenna structure. Moreover, the frequency behavior of the modes reveals interesting wideband properties of the structure. However, the tracking of characteristic modes over frequency remains a challenge, especially when differences between modes is limited to high currents in small regions of the structure. The common approach of tracking characteristic modes is through correlating the modal currents over frequency, this leads to multiple eigenvalues being mapped to the same eigencurrent. In this work, we propose a new approach to track characteristic modes by means of cross correlating far-field patterns, which effectively eliminates the mode mapping ambiguity.

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Section: Characteristic Mode Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Wideband Characteristic Mode Tracking Utilizing Far-Field Patterns

Miers

Lau

2015

Antennas Wirel. Propag. Lett.

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Preprocessing for characteristic mode tracking based on the correlation

Liu

Yang

et al. 2020

Int J Numerical Modelling

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Mode swapping in characteristic mode analysis (CMA) affects consistent interpretation of the modal data. Mode tracking based on the eigenvector correlation can effectively determine the correct ordering of modes at each sampling frequency. However, there has been a lack of corresponding preprocessing for this mode-tracking algorithm. For example, the applicable frequency band cannot be determined in advance. In this paper, cosine similarity is used to calculate the correlation between modes of adjacent frequencies. It is then proposed that the 3/2 proportional relationship between the maximum size of the structure and the minimum operating wavelength can be used to choose the reliable operating band range of the algorithm. Meanwhile, it is recommended that the meshing side length should be less than one-tenth of a wavelength, and the adjacent sampling frequency interval should be less than the relative bandwidth of 10%. Through this preprocessing, correlation-based algorithms can efficiently implement mode tracking for open conductors.

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Accelerated numerical modeling of RF circuits using network characteristic mode analysis

Jin

Hajiaboli³

et al. 2021

Int J Numerical Modelling

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A fast numerical modeling approach based on network characteristic mode analysis (CMA) is presented and investigated for analyzing electrical layouts in miniature RF filters, such as surface acoustic wave filters. In this approach, a generalized eigenvalue decomposition is performed on the Z‐parameters of an electrical layout at one or two sampling frequencies that can be computed and extracted with any numerical full‐wave method. The obtained eigenvalues are used to extract modal resistance, inductance, and capacitance matrices for each eigenmode. The frequency dependence of the modal resistance matrix can be assumed a priori or determined automatically, and the modal inductance and capacitance matrices are assumed frequency independent. These modal matrices are then used to approximate the Z‐parameters at any other frequencies to provide the response of the electrical layout, which can then be combined with the frequency responses of other components, such as resonators, to yield the electrical response of an entire RF filter. Compared with the previously developed analytic extension of eigenvalues, this fast CMA‐based method is less affected by the frequency variation of eigenmodes since the frequency dependences of the eigenmodes are implicitly canceled out in its formulation. The accuracy of this approach is evaluated by comparing with results from full‐wave analyses. For RF circuits whose electrical sizes are small and whose frequency range of interest is relatively small, the proposed CMA‐based fast frequency sweep approach is found to be sufficiently accurate and highly practical for engineering applications.

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A hybrid tracking algorithm for characteristic mode analysis

Cited by 21 publications

References 9 publications

Wideband Characteristic Mode Tracking Utilizing Far-Field Patterns

Wideband Characteristic Mode Tracking Utilizing Far-Field Patterns

Preprocessing for characteristic mode tracking based on the correlation

Accelerated numerical modeling of RF circuits using network characteristic mode analysis

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