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高效准确的阵列模式合成

Efficient and Accurate Synthesis for Array Pattern Shaping.

机构信息

Division of Electrical, Electronic, and Control Engineering, Kongju National University, Cheonan 31080, Korea.

Department of Mechanical Engineering, Gangneung-Wonju National University, Wonju 26403, Korea.

出版信息

Sensors (Basel). 2022 Jul 25;22(15):5537. doi: 10.3390/s22155537.

DOI:10.3390/s22155537
PMID:35898046
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9331809/
Abstract

Array pattern synthesis (APS) aims to create the desired array pattern as closely as possible to the prescribed mask template by varying the element excitations of the array. Herein, an efficient approach for the APS to control the sidelobe level is proposed. After designing the mask template to meet the prescribed sidelobe requirements and the waveform pattern, a set of element excitations is calculated through the Fourier transform performed on the projection the waveform pattern onto the mask template. Then, a desired array pattern can be synthesized from this updated set of excitation coefficients. The proposed APS approach directly presents a mathematical formulation of the exact set of excitations without any iterative optimization process. The proposed method is particularly suited for many array elements in linear antenna array. Thus, the proposed APS achieves substantial improvements in terms of computation complexity, performance, and ease of implementation in the algorithm when compared with conventional methods. Several simulation results are provided to verify the efficacy and effectiveness of the proposed method.

摘要

阵列模式综合(APS)旨在通过改变阵列单元的激励来尽可能接近地创建所需的阵列模式,以满足规定的掩模模板。本文提出了一种用于 APS 以控制旁瓣电平的有效方法。在设计掩模模板以满足规定的旁瓣要求和波形模式之后,通过将波形模式投影到掩模模板上进行傅里叶变换来计算一组单元激励。然后,可以从这个更新后的激励系数集合成期望的阵列模式。所提出的 APS 方法直接给出了精确激励集的数学公式,而无需任何迭代优化过程。该方法特别适用于线性天线阵中的许多阵元。因此,与传统方法相比,所提出的 APS 在算法的计算复杂度、性能和实现难易程度方面都有了显著的提高。提供了一些仿真结果来验证所提出方法的有效性和有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/31d8136257a4/sensors-22-05537-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/22621c18c78a/sensors-22-05537-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/2a7e77fcc475/sensors-22-05537-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/da0067d2a207/sensors-22-05537-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/31d8136257a4/sensors-22-05537-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/22621c18c78a/sensors-22-05537-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/2a7e77fcc475/sensors-22-05537-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/da0067d2a207/sensors-22-05537-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c81b/9331809/31d8136257a4/sensors-22-05537-g004.jpg

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ISAR Imaging of High-Speed Maneuvering Target Using Gapped Stepped-Frequency Waveform and Compressive Sensing.基于频移步进和压缩感知的高速机动目标 ISAR 成像
IEEE Trans Image Process. 2017 Oct;26(10):5043-5056. doi: 10.1109/TIP.2017.2728182. Epub 2017 Jul 17.