Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 170

The DOI matches its Crossref record.

Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy

Anyong Qing·2003·IEEE Transactions on Antennas and Propagation

Abstract

Electromagnetic inverse scattering of multiple two-dimensional (2-D) perfectly conducting objects with transverse magnetic (TM) wave incidence by the differential evolution strategy (DES) is presented. The governing electric field integral equations for the scattering problem are expressed as surface integral over the cylinder contours. The cylinder contours are approximately represented by closed cubic B-splines local shape functions in local polar coordinate system. The inverse problem is to locate the cylinders and to reconstruct their shape with or without a priori knowledge of the number of cylinders. It is cast into an optimization problem and is solved using the DES. Both synthetic and real reconstructions are carried out. The reconstruction results agree with the true profiles very well. Comparison with the real-coded genetic algorithm has been carried out. It has been observed that the DES outperforms the real-coded genetic algorithm.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tap.2003.811492

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 170

Metadata

Authors
Qing, A.
Venue
IEEE Transactions on Antennas and Propagation
Published
2003-06
Volume
51
Issue
6
Pages
1251-1262
ISSN
0018-926X (ISSN-L)
OpenAlex
W2107830363
Cited by
170
References
0
FWCI
7.43
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy. https://app.cochat.ai/papers/10.1109/tap.2003.811492
The paper
Qing, A. (2003). Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy. IEEE Transactions on Antennas and Propagation. https://doi.org/10.1109/tap.2003.811492

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.