Design of multiple-feed lens antennas requires multivariate and multi-objective optimization processes,which can be accelerated by PSO algorithms.However,the PSO algorithm often fails to achieve optimal results with l...Design of multiple-feed lens antennas requires multivariate and multi-objective optimization processes,which can be accelerated by PSO algorithms.However,the PSO algorithm often fails to achieve optimal results with limited computation resources since spaces of candidate solutions are quite large for lens antenna designs.This paper presents a design paradigm for multiple-feed lens antennas based on a physics-assisted particle swarm optimization(PA-PSO)algorithm,which guides the swarm of particles based on laws of physics.As a proof of concept,a design of compact metalens antenna is proposed,which measures unprecedented performances,such as a field of view at±55°,a 21.7 dBi gain with a flatness within 4 dB,a 3-dB bandwidth>12°,and a compact design with a f-number of 0.2.The proposed PA-PSO algorithm reaches the optimal results 6 times faster than the ordinary PSO algorithm,which endows promising applications in the multivariate and multi-objective optimization processes,including but not limited to metalens antenna designs.展开更多
利用分数阶傅里叶变换(fractional Fourier transform,FRFT)对线性调频(linear frequency modulation,LFM)信号的能量匹配聚焦特性来简化共形阵列的数据模型,根据子空间拟合原理提出一种信源DOA和极化参数的去耦联合估计方法.直接进行DO...利用分数阶傅里叶变换(fractional Fourier transform,FRFT)对线性调频(linear frequency modulation,LFM)信号的能量匹配聚焦特性来简化共形阵列的数据模型,根据子空间拟合原理提出一种信源DOA和极化参数的去耦联合估计方法.直接进行DOA估计涉及求解难度较大的多维多峰参数搜索过程,于是通过重构噪声子空间和流形矩阵建立了单峰的目标函数,然后用PSO算法估计信源方位角和俯仰角,在此基础上利用ESPRIT实现极化参数估计.仿真实验表明,去耦参数估计方法能在保证算法性能的前提下简化问题复杂度。展开更多
基金supported by the National Natural Science Foundation of China(61975026,62375232,6237523262205246 and 61875030)Creative Research Groups of the National Natural Science Foundation of Sichuan Province(2023NSFSC1973)+1 种基金the Shanghai Pilot Program for Basic Research,the National Key Research and Development Program of China(No.2023YFF0613600)Science and Technology Commission of Shanghai Municipality(No.22ZR1432400).
文摘Design of multiple-feed lens antennas requires multivariate and multi-objective optimization processes,which can be accelerated by PSO algorithms.However,the PSO algorithm often fails to achieve optimal results with limited computation resources since spaces of candidate solutions are quite large for lens antenna designs.This paper presents a design paradigm for multiple-feed lens antennas based on a physics-assisted particle swarm optimization(PA-PSO)algorithm,which guides the swarm of particles based on laws of physics.As a proof of concept,a design of compact metalens antenna is proposed,which measures unprecedented performances,such as a field of view at±55°,a 21.7 dBi gain with a flatness within 4 dB,a 3-dB bandwidth>12°,and a compact design with a f-number of 0.2.The proposed PA-PSO algorithm reaches the optimal results 6 times faster than the ordinary PSO algorithm,which endows promising applications in the multivariate and multi-objective optimization processes,including but not limited to metalens antenna designs.
文摘利用分数阶傅里叶变换(fractional Fourier transform,FRFT)对线性调频(linear frequency modulation,LFM)信号的能量匹配聚焦特性来简化共形阵列的数据模型,根据子空间拟合原理提出一种信源DOA和极化参数的去耦联合估计方法.直接进行DOA估计涉及求解难度较大的多维多峰参数搜索过程,于是通过重构噪声子空间和流形矩阵建立了单峰的目标函数,然后用PSO算法估计信源方位角和俯仰角,在此基础上利用ESPRIT实现极化参数估计.仿真实验表明,去耦参数估计方法能在保证算法性能的前提下简化问题复杂度。
