The Adaptive Cross Approximation Algorithm for Accelerated Method of Moments Computations of EMC Problems

Zhao, Kun; Vouvakis, Marinos N.; Lee, Jin‐Fa

doi:10.1109/temc.2005.857898

Cited by 557 publications

(385 citation statements)

References 33 publications

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“…Thus, the FACA can provide a more efficient compression of an impedance submatrix between two well-separated blocks in the MoM than the conventional ACA [1]. Here, the compression of a low-rank matrix M ∈ C m×n is set to be an example to describe the FACA as follows.…”

Section: Principle Of Facamentioning

confidence: 99%

“…Thus, the computation time of the monostatic RCS is remarkably reduced with the help of the FACA. The conventional ACA [1] has been used to speed up the monostatic RCS calculation in [2], in which the excitation matrix is compressed as…”

Section: Compression Of Voltage Matrixmentioning

confidence: 99%

“…The adaptive cross approximation (ACA)-based algorithms [1][2][3][4][5][6][7] are relatively popular in recent years. For perfect electric conducting (PEC) targets within 50 wavelengths, the sparsified ACA (SPACA) algorithm [4,5], which can be considered as a fast ACA (FACA)-based algorithm [7], is very efficient.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in this paper, the FACA [7] is employed to compress the excitation matrix so as to reduce the number of iterative solutions of the matrix equation. Compared with the conventional ACA [1,8], the FACA can save storage and give a more efficient sampling procedure for decomposing the excitation matrix when dealing with electrically large problems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Method to Accelerate the Calculation of Two-Dimensional Monostatic Radar Cross Section on Pec Targets

Fei¹,

Chen²,

Zhang³

et al. 2016

PIER M

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Abstract-This paper proposes a hybrid method to accelerate the calculation of the monostatic radar cross section (RCS) of perfect electric conducting (PEC) targets. In a sense, the proposed method can be considered as a fast adaptive cross approximation (FACA)-based method. The FACA is firstly used to compress the excitation matrix which comes from the beforehand defined incident plane waves. It decreases the time and memory on decomposing the excitation matrix compared with the conventional adaptive cross approximation (ACA). Furthermore, the computational complexity of iterative solution is reduced by using the sparsified ACA (SPACA) algorithm after dividing the target into blocks. Consequently, the proposed method is efficient for calculating two-dimensional (2D) monostatic RCS.

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Section: Principle Of Facamentioning

confidence: 99%

Section: Compression Of Voltage Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Method to Accelerate the Calculation of Two-Dimensional Monostatic Radar Cross Section on Pec Targets

Fei¹,

Chen²,

Zhang³

et al. 2016

PIER M

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“…The Multilevel FMM (MLFMM) for the SRM was later introduced by [10] and used in combination with higher order basis functions in [11]. It is also worth mentioning the computational efficiency achieved by the Adaptive Cross Approximation algorithm (ACA) approach for the SRM [4,33] and by its multilevel version [28]. All these approaches make use of the available memory resources in order to reduce the calculation time.…”

Section: Introductionmentioning

confidence: 99%

Fast Antenna Characterization Using the Sources Reconstruction Method on Graphics Processors

López-Fernández¹,

López-Portugués²,

Álvarez³

et al. 2012

PIER

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Abstract-The Sources Reconstruction Method (SRM) is a noninvasive technique for, among other applications, antenna characterization. The SRM is based on obtaining a distribution of equivalent currents that radiate the same field as the antenna under test. The computation of these currents requires solving a linear system, usually ill-posed, that may be very computationally demanding for commercial antennas. Graphics Processing Units (GPUs) are an interesting hardware choice for solving compute-bound problems that are prone to parallelism. In this paper, we present an implementation on GPUs of the SRM applied to antenna characterization that is based on a compute-bound algorithm with a high degree of parallelism. The GPU implementation introduced in this work provides a dramatic reduction on the time cost compared to our CPU implementation and, in addition, keeps the low-memory footprint of the latter. For the sake of illustration, the equivalent currents are obtained on a base station antenna array and a helix antenna working at practical frequencies. Quasi real-time results are obtained on a desktop workstation.

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Multiscale electromagnetic computations using a hierarchical multilevel fast multipole algorithm

2014

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A hierarchical multilevel fast multipole method (H-MLFMM) is proposed herein to accelerate the solutions of surface integral equation methods. The proposed algorithm is particularly suitable for solutions of wideband and multiscale electromagnetic problems. As documented in Zhao and Chew (2000) that the multilevel fast multipole method (MLFMM) achieves O(N log N) computational complexity in the fixed mesh size scenario, hk = cst, where h is the mesh size and k is the corresponding wave number, for problems discretized under conventional mesh density. However, its performance deteriorates drastically for overly dense meshes where the couplings between different groups are dominated by evanescent waves or circuit physics. In the H-MLFMM algorithm, two different types of basis functions are proposed to address these two different natures of physics corresponding to the electrical size of the elements. Specifically, for the propagating wave couplings, the plane wave basis function adopted by MLFMM are effective and they are inherited by H-MLFMM. Whereas in the circuit physics and for the evanescent waves, H-MLFMM employs the so-called skeleton basis. Moreover, the proposed H-MLFMM unifies the procedures to account for the couplings using these two distinct types of basis functions. O(N) complexity is observed for both memory and CPU time from a set of numerical examples with fixed mesh sizes. Numerical results are included to demonstrate that H-MLFMM is error controllable and robust for a wide range of applications.

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The Adaptive Cross Approximation Algorithm for Accelerated Method of Moments Computations of EMC Problems

Cited by 557 publications

References 33 publications

A Hybrid Method to Accelerate the Calculation of Two-Dimensional Monostatic Radar Cross Section on Pec Targets

A Hybrid Method to Accelerate the Calculation of Two-Dimensional Monostatic Radar Cross Section on Pec Targets

Fast Antenna Characterization Using the Sources Reconstruction Method on Graphics Processors

Multiscale electromagnetic computations using a hierarchical multilevel fast multipole algorithm

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