Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar
Abstract The performance of the distributed coherent aperture radar (DCAR) is heavily influenced by the antenna positions. Therefore, an antenna position optimization method is proposed based on the adaptive genetic algorithm with a self‐supervised differential operator. In the proposed method, the...
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2021-07-01
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Series: | IET Radar, Sonar & Navigation |
Online Access: | https://doi.org/10.1049/rsn2.12055 |
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doaj-dd707bb9f0e04c75affd0af9dbca91af2021-08-02T08:30:41ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922021-07-0115767768510.1049/rsn2.12055Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radarXiaopeng Yang0Yuqing Li1Feifeng Liu2Tian Lan3Long Teng4Tapan K. Sarkar5School of Information and Electronics Beijing Institute of Technology Beijing ChinaSchool of Information and Electronics Beijing Institute of Technology Beijing ChinaSchool of Information and Electronics Beijing Institute of Technology Beijing ChinaChongqing Innovation Center Beijing Institute of Technology Chongqing ChinaSchool of Information and Electronics Beijing Institute of Technology Beijing ChinaDepartment of Electrical Engineering and Computer Science Syracuse University Syracuse New York, USAAbstract The performance of the distributed coherent aperture radar (DCAR) is heavily influenced by the antenna positions. Therefore, an antenna position optimization method is proposed based on the adaptive genetic algorithm with a self‐supervised differential operator. In the proposed method, the antenna positions are firstly coded as the chromosomes of the population with multiple constraints, and the reciprocal of the peak side lobe level (PSLL) of the beam pattern is calculated as the fitness function for optimization. Then, the adaptive probabilities are calculated for the crossover and mutation of chromosomes and a self‐supervised differential operator is utilized in the mutation. Finally, the optimal antenna positions for DCAR can be obtained with the lowest PSLL compared with the existing methods. The effectiveness of the proposed method is verified by linear and planar DCARs, respectively.https://doi.org/10.1049/rsn2.12055 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaopeng Yang Yuqing Li Feifeng Liu Tian Lan Long Teng Tapan K. Sarkar |
spellingShingle |
Xiaopeng Yang Yuqing Li Feifeng Liu Tian Lan Long Teng Tapan K. Sarkar Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar IET Radar, Sonar & Navigation |
author_facet |
Xiaopeng Yang Yuqing Li Feifeng Liu Tian Lan Long Teng Tapan K. Sarkar |
author_sort |
Xiaopeng Yang |
title |
Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar |
title_short |
Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar |
title_full |
Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar |
title_fullStr |
Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar |
title_full_unstemmed |
Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar |
title_sort |
antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar |
publisher |
Wiley |
series |
IET Radar, Sonar & Navigation |
issn |
1751-8784 1751-8792 |
publishDate |
2021-07-01 |
description |
Abstract The performance of the distributed coherent aperture radar (DCAR) is heavily influenced by the antenna positions. Therefore, an antenna position optimization method is proposed based on the adaptive genetic algorithm with a self‐supervised differential operator. In the proposed method, the antenna positions are firstly coded as the chromosomes of the population with multiple constraints, and the reciprocal of the peak side lobe level (PSLL) of the beam pattern is calculated as the fitness function for optimization. Then, the adaptive probabilities are calculated for the crossover and mutation of chromosomes and a self‐supervised differential operator is utilized in the mutation. Finally, the optimal antenna positions for DCAR can be obtained with the lowest PSLL compared with the existing methods. The effectiveness of the proposed method is verified by linear and planar DCARs, respectively. |
url |
https://doi.org/10.1049/rsn2.12055 |
work_keys_str_mv |
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1721238207703547904 |