scholarly journals Efficient Computation of Wideband RCS Using Singular Value Decomposition Enhanced Improved Ultrawideband Characteristic Basis Function Method

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Wen-yan Nie ◽  
Zhong-gen Wang
Keyword(s):  
Singular Value Decomposition ◽  
Basis Function ◽  
Matrix Equation ◽  
Function Method ◽  
Plane Waves ◽  
Singular Value ◽  
Efficient Computation ◽  
Incident Plane ◽  
Filling Time ◽  
Value Decomposition

The singular value decomposition (SVD) enhanced improved ultrawideband characteristic basis function method (IUCBFM) is proposed to efficiently analyze the wideband scattering problems. In the conventional IUCBFM, the SVD is only applied to reduce the linear dependency among the characteristic basis functions (CBFs) due to the overestimation of incident plane waves. However, the increase in the size of the targets under analysis will require a large number of incident plane waves and it will become very time-consuming to solve such numbers of the matrix equation. In this paper, the excitation matrix is compressed by using the SVD in order to reduce both the number of matrix equation solutions and the number of CBFs compared with the traditional IUCBFM. Furthermore, the dimensions of the reduced matrix and the reduced matrix filling time are significantly reduced. Numerical results demonstrate that the proposed method is accurate and efficient.

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.

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.

Optimal Approximate Solution of a Class of Matrix Equation

2012 ◽  
Vol 220-223 ◽  
pp. 2180-2183
Author(s):  
Chun Rui Cheng ◽  
Jun Hui Zhu
Keyword(s):  
Approximate Solution ◽  
Singular Value Decomposition ◽  
Matrix Equation ◽  
Symmetric Solution ◽  
Singular Value ◽  
Solution Set ◽  
Optimal Approximate Solution ◽  
Generalized Singular Value Decomposition ◽  
Value Decomposition ◽  
Matrix Pair

The optimal approximate solution of a class of matrix equation is considered. Based on the generalized singular value decomposition, the expression of the optimal approximate solution to a given matrix pair is derived in the symmetric solution set.

scholarly journals NDF of Scattered Fields for Strip Geometries

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 202
Author(s):  
Ehsan Akbari Sekehravani ◽  
Giovanni Leone ◽  
Rocco Pierri
Keyword(s):  
Degrees Of Freedom ◽  
Plane Waves ◽  
Singular Value ◽  
Scattering Operator ◽  
Scattering Problems ◽  
First Order ◽  
Incident Plane ◽  
Minimum Number ◽  
Scattered Fields ◽  
Value Decomposition

Solving inverse scattering problems by numerical methods requires investigating the number of independent pieces of information that can be reconstructed stably. To this end, we address the evaluation of the Number of Degrees of Freedom (NDF) of far-zone scattered fields for some strip geometries under the first-order Born approximation. The analysis is performed by employing the Singular Value Decomposition (SVD) of the scattering operator in the two-dimensional scalar geometry of one or more strips illuminated by a TM polarized plane wave. It is known that investigating the scattering scene at different incident plane waves (multi-view configuration) enhances the NDF. Therefore we mean to examine the minimum number of incident plane waves providing the NDF of the scattered fields both by theoretical estimations and numerical verifications.

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