Fast Frequency Sweep for Multiple PEC Objects RCS Computation Based on the Characteristic Basis Function Method

2014 ◽  
Vol 667 ◽  
pp. 345-348
Author(s):  
Jie Liu ◽  
Wei Lai Li ◽  
Jian Jun Pan ◽  
Zhong Kuan Chen
Keyword(s):  
Method Of Moments ◽  
Basis Function ◽  
Function Method ◽  
Wide Band ◽  
Basis Functions ◽  
Computational Time ◽  
Matrix Equations ◽  
Electric Conducting ◽  
Band Characteristic ◽  
Wideband Radar

To obtain wideband radar cross-section (RCS) frequency response of multiple perfectly electric conducting (PEC) objects, the frequency sweeping by reusing the ultra-wide band characteristic basis functions (UCBFs) is applied. This method, based on the Characteristic Basis Function Method (CBFM), maintains all the benefit of CBFM, especially accelerating the solution of matrix equations generated by the method of moments (MoM) applied to the scatting problems in electromagnetics. Compared with conventional CBFM procedure, reusing the UCBFs without repeating the calculations of them at different frequency points leads to a significant reduction of computational time. Generating UCBFs for highest frequency, reusing UCBFs for lower frequencies and constructing reduced matrix for each frequency are the three keys of this method. Numerical results demonstrated the efficiency of this method.

Ultra-wideband characteristic basis function method merging the secondary level characteristic basis functions for rapid computation of wideband radar cross section problems from conducting targets

2020 ◽  
pp. 1-10
Author(s):  
Zhong-Gen Wang ◽  
Jun-Wen Mu ◽  
Wen-Yan Nie
Keyword(s):  
Basis Function ◽  
Function Method ◽  
Secondary Level ◽  
Singular Value ◽  
Basis Functions ◽  
Ultra Wideband ◽  
Scattering Problems ◽  
Wideband Radar ◽  
Value Decomposition ◽  
A Current

In this paper, a merged ultra-wideband characteristic basis function method (MUCBFM) is presented for high-precision analysis of wideband scattering problems. Unlike existing singular value decomposition (SVD) enhanced improved ultra-wideband characteristic basis function method (SVD-IUCBFM), the MUCBFM reduces the number of characteristic basis functions (CBFs) necessary to express a current distribution. This reduction is achieved by combining primary CBFs (PCBFs) with the secondary level CBFs (SCBFs) to form a single merged ultra-wideband characteristic basis function (MUCBF). As the MUCBF incorporates the effects of PCBFs and SCBFs, the accuracy does not change significantly compared to that obtained by the SVD-IUCBFM. Furthermore, the efficiencies of constructing the CBFs and filling the reduced matrix are improved. Numerical examples verify and demonstrate that the proposed method is credible both in terms of accuracy and efficiency.

scholarly journals Application of the Characteristic Basis Function Method Using CUDA

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Juan Ignacio Pérez ◽  
Eliseo García ◽  
José A. de Frutos ◽  
Felipe Cátedra
Keyword(s):  
Method Of Moments ◽  
Basis Function ◽  
Function Method ◽  
Matrix Equations ◽  
Compute Unified Device Architecture ◽  
Device Architecture ◽  
Graphics Processing ◽  
Computing Infrastructure

The characteristic basis function method (CBFM) is a popular technique for efficiently solving the method of moments (MoM) matrix equations. In this work, we address the adaptation of this method to a relatively new computing infrastructure provided by NVIDIA, the Compute Unified Device Architecture (CUDA), and take into account some of the limitations which appear when the geometry under analysis becomes too big to fit into the Graphics Processing Unit’s (GPU’s) memory.

scholarly journals Broadband Adaptive RCS Computation through Characteristic Basis Function Method

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Guohua Wang ◽  
Yufa Sun
Keyword(s):  
Cross Section ◽  
Basis Function ◽  
Function Method ◽  
High Accuracy ◽  
Wide Frequency Range ◽  
Singular Value ◽  
Basis Functions ◽  
Frequency Range ◽  
Arbitrary Frequency ◽  
Value Decomposition

A broadband radar cross section (RCS) calculation approach is proposed based on the characteristic basis function method (CBFM). In the proposed approach, the desired arbitrary frequency band is adaptively divided into multiple subband in consideration of the characteristic basis functions (CBFs) number, which can reduce the universal characteristic basis functions (UCBFs) numbers after singular value decomposition (SVD) procedure at lower subfrequency band. Then, the desired RCS data can be obtained by splicing the RCS data in each subfrequency band. Numerical results demonstrate that the proposed method achieve a high accuracy and efficiency over a wide frequency range.

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