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基于智能算法的超材料快速优化设计方法研究进展

贾宇翔 王甲富 陈维 随赛 朱瑞超 邱天硕 李勇峰 韩亚娟 屈绍波

贾宇翔, 王甲富, 陈维, 等. 基于智能算法的超材料快速优化设计方法研究进展[J]. 雷达学报, 2021, 10(2): 220–239. doi: 10.12000/JR21027
引用本文: 贾宇翔, 王甲富, 陈维, 等. 基于智能算法的超材料快速优化设计方法研究进展[J]. 雷达学报, 2021, 10(2): 220–239. doi: 10.12000/JR21027
WANG Xinshuo, LU Jingyue, MENG Zhichao, et al. Forward-looking multi-channel synthetic aperture radar imaging and array attitude error compensation[J]. Journal of Radars, 2023, 12(6): 1155–1165. doi: 10.12000/JR23073
Citation: JIA Yuxiang, WANG Jiafu, CHEN Wei, et al. Research progress on rapid optimization design methods of metamaterials based on intelligent algorithms[J]. Journal of Radars, 2021, 10(2): 220–239. doi: 10.12000/JR21027

基于智能算法的超材料快速优化设计方法研究进展

DOI: 10.12000/JR21027 CSTR: 32380.14.JR21027
基金项目: 国家自然科学基金(61971435, 61971341, 61801509, 61901508),科技部国家重点研发计划(2017YFA0700201)
详细信息
    作者简介:

    贾宇翔(1993–),男,河北衡水人,2016年在空军工程大学获电子科学与技术专业硕士学位,现在空军工程大学基础部攻读博士学位,主要研究方向为基于人工表面等离激元的电磁散射调控。E-mail: jiayuxiang93@163.com

    王甲富(1981–),男,山东聊城人,教授,博士生导师,博士学位论文获2012年“全国优秀博士学位论文”提名,荣立个人三等功三次。主要研究方向为超材料设计及其在微波器件中的应用,目前已发表学术论文390余篇。E-mail: wangjifu1981@126.com

    陈 维(1982–),男,陕西三原人,2009年在空军工程大学导弹学院军事装备学获硕士学位,现为93704部队任雷达工程师,主要研究方向为制导雷达指控仓。E-mail: chenwei918113@126.com

    随 赛(1993–),男,安徽亳州人,博士,讲师,2019年毕业于空军工程大学,现任职于空军工程大学,主要研究方向为隐身新材料与新技术,目前已发表学术论文35篇。E-mail: suisai_mail@foxmail.com

    朱瑞超(1996–),男,山东济南人,2020年在空军工程大学获电子科学与技术专业硕士学位,现在空军工程大学基础部攻读博士学位。主要研究方向为基于智能算法的超表面设计。E-mail: zhuruichao1996@163.com

    邱天硕(1992–),男,吉林长春人,博士,讲师。2019年于空军工程大学获得博士学位,现任空军工程大学基础部讲师。主要研究方向为智能材料与设计、有源超材料等。E-mail: qiutianshuo1992@163.com

    李勇峰(1986–),男,甘肃平凉人,2010年在空军工程大学获物理电子学硕士学位,2015年在空军工程大学获物理电子学博士学位。现为空军工程大学基础部讲师,主要研究方向为电磁超构表面、天线、隐身材料与隐身技术、雷达目标特性仿真评估等,目前已发表学术论文40余篇。E-mail: liyf217130@126.com

    韩亚娟 (1989–),女,陕西礼泉人,博士,讲师。2020年获西安电子科技大学博士学位,现担任空军工程大学基础部讲师,主要研究方向为超表面、人工表面等离激元及其在天线设计中的应用。目前已发表学术论文30余篇。E-mail: mshyj_mail@126.com

    屈绍波(1965–),男,安徽亳州人,教授,博士生导师,全国模范教师,全军学科拔尖人才,空军级专家,享受政府特殊津贴,荣立个人二等功一次。主要研究方向为材料物理、超材料,目前已发表学术论文490余篇。承担各类课题60余项,国家自然科学基金项目20余项。E-mail: qushaobo@mail.xjtu.edu.cn

    通讯作者:

    王甲富 wangjifu1981@126.com

  • 责任主编:李龙 Corresponding Editor: LI Long
  • 中图分类号: TB34

Research Progress on Rapid Optimization Design Methods of Metamaterials Based on Intelligent Algorithms

Funds: The National Natural Science Foundation of China (61971435, 61971341, 61801509, 61901508), The National Key Research and Development Program of China (2017YFA0700201)
More Information
  • 摘要: 目前,超材料研究不断向工程化应用推进,在物理机理与效应、设计理论与方法、加工制备与测试等方面取得了突飞猛进的发展。但是,传统的超材料设计主要依赖人工设计和优化,面对大规模的工程化应用设计时,无法实现数量庞大的超材料结构单元的快速整体设计。近几年,涵盖传统启发式算法和神经网络算法的智能算法在超材料设计中所占的比重逐步上升,基于智能算法设计超材料能够打破传统设计方法在不同基材体系、不同频段以及不同性能指标下设计的局限性,展现出快速设计和架构创新的独特优势。该文综述了包括遗传算法、Hopfield网络算法和深度学习在内的几种典型智能算法在超材料设计中的应用,包括正向设计方法和逆向设计方法。基于智能算法能够实现不同性能指标的频率选择表面、多机理复合吸波超材料、平板聚焦超表面以及异常反射超表面的快速设计,为推动超材料技术的工程化应用提供必要设计手段支撑。

     

  • High resolution radar imaging has been widely used in target scattering diagnostics and recognition. As we all know, high resolution in range dimension is derived from the bandwidth of the transmitting signal and in the cross range dimension from synthetic aperture of multiple spatial positions. Under the fixed bandwidth and the synthetic aperture, traditional Matched Filter (MF) based methods for radar imaging suffer from low resolution and high sidelobes limited by the synthetic aperture[1].

    In order to improve the resolution and suppress the sidelobes, many high resolution methods have been applied to radar imaging. For example, the recently introduced theory of Compressed Sensing (CS) provides an idea to improve the resolution and reduce the amounts of measurement data under the constraint of sparsely distributed target prior, which has been widely explored for applications of radar imaging[24]. However, conventional CS methods are confronted with a range of problems in practical scenarios, such as complexity in calculation, high Signal-to-Noise Ratio (SNR) requirement, model mismatch caused by off grid problem[5], phase mismatch[6], frequency error[7] and position error[8]. To avoid the off grid problem of CS, modern spectral estimation methods like MUltiple SIgnal Classification (MUSIC), matrix pencil and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) have been used in radar imaging for resolution improvement[9]. However, most those methods suffer from performance degradation when there is little prior knowledge of the exact numbers of the scatters or under low SNR condition. Recently, the atomic norm minimization algorithm[10] based on continuous compressed sensing is introduced to enhance the SNR of the received echo and using Vandermonde decomposition to eliminate the grid mismatch. Nevertheless, this method can only be tailored to a specific model and brings huge computational cost.

    Consideration the aforementioned fact while combining the sparsity low rank matrix recovery technology and deconvolution algorithm, we introduce a high resolution radar imaging method based on the MF result. Firstly we establish the convolution model of target’s backscatter coefficients and the Point Spread Function (PSF), and then we want to use the deconvolution method like Wiener filter to improve the radar imaging resolution. However, the performance improvements of those methods depend on high SNR, and their super resolution performance is visibly affected by the low pass character of the PSF[11]. Although the MF result has enhanced the SNR, we can further improve the echo SNR by the sparsity and low rank matrix recovery. Low rank matrix recovery has been applied in many signal processing applications to estimate a low rank matrix from its noisy observation[12, 13]. Combinng the sparsity of the echo matrix, we modify the low rank matrix recovery and introduce it to radar echo denoising, which can improve the performance of the two-Dimensional (2D) deconvolution. Finally, some experimental results are conducted to verify the effectiveness of the proposed method.

    Notation: (·)T, (·)H and (·)* denote the transpose, the conjugate transpose and the conjugate operation, respectively. ,﹡, and ☉ indicate the inner product, the convolution and the Hadamard product. F, 1, are the Frobenius norm, sum of the absolute values and the nuclear norm.

    Considering a typical arrangement for radar imaging in which an object with scattering reflectivity σxy rotated by a scan angle θm (as shown in Fig. 1), we defined the positions of the transmitting and receiving antenna shown in a Cartesian coordinate as (W/2, –R, H) and (–W/2, –R,H), where W, R, H represent the antenna spacing, distance from antenna to XZ plane and XY plane, respectively.

    Figure  1.  Radar imaging geometry

    Transmitting a stepped-frequency signal with frequency fn and under Born approximation, the received scattered echo with Gauss noise Wmn is given by:

    Ymn=Sσxyej2πfnR(x,y;θm)/cdxdy+Wmn (1)

    In this equation: fn=f0+nΔf,n=0,1···,N1, f0 and Δf represent the start frequency and frequency step, θm=mΔθ,m=0,1,···,M, Δθ represents the rotating angle step, respectively. The range R(x,y;θm) from the transmitting antenna to echo scattering center and to the receiving antenna can be calculated as Eq. (2):

    R(x,y;θm)=(xcosθmysinθm+W2)2+(xsinθm+ycosθm+R)2+H2+(xcosθmysinθmW2)2+(xsinθm+ycosθm+R)2+H2 (2)

    In far-field and small rotation angle case, R(x,y;θm) can be approximated by first order Taylor-series expansion as:

    R(x,y;θm)2(R0+(x+mΔθy)R/R0) (3)

    where R0=R2+(W/2)2+H2.

    Then the received echo can be written as follow under some approximated conditions:

    ˜Ymn=S˜σxyej4πRΔθλR0mxej4πRΔfR0cnydxdy+Wmn (4)

    where ˜σxy=σxyej4πRy/λR0, ˜Ymn=Ymnej4πfnR0/c, λc/f0.

    After discrete imaging region with P×Q grids, the received echo in Eq. (4) can be described as the following 2D linear signal model:

    ˜Y=Ax˜ΣATy+W (5)

    where ˜Y=[  ˜Ymn]M×N is the echo matrix, ˜Σ=[˜σpq]P×Q is the observation matrix: Ax=[ej4πmxpRΔθ/λR0]M×P, Ay=[ej4πnyqRΔf/R0c]N×Q.

    Considering the targets present sparse point scattering characteristic under high frequency scattering in most practical application scenarios, we present our method to improve the resolution of radar imaging under sparse target constraint using 2D deconvolution algorithm with low rank sparsity echo matrix denoising.

    As we all know, the MF algorithm which is based on the maximum signal to noise ratio is the most stable and commonly used radar imaging method. However, due to limitation of the synthetic aperture and bandwidth, the standard MF method suffers from relatively low resolution and high sidelobes, especially under the requirements of high resolution. The received echo after MF from Eq. (5) can be obtained by:

    YMF=AHx  ˜YAy (6)

    From the result of Eq. (6), the echo of the surface target after MF can be described as the sum of all the wave scattered at the points on the surface grid, i.e.,

    YMF(x,y)=xy˜σ(x,y)Psf(xx,yy) (7)

    where we define the PSF as:

    Psf(xx,yy)=ax(x),ax(x)ay(y),ay(y) (8)

    here, ax(x) and ay(y) represent the column of matrix Ax and Ay at the grid (x, y).

    We can find that Eq. (6) can be seen as the 2D convolution of the PSF and target backscatter coefficients:

    YMF(x,y)=˜σ(x,y)Psf(x,y)+WMF(x,y) (9)

    Inspired by this, we can recovery the backscatter coefficients using deconvolution algorithm to improve the imaging quality. Firstly, we should analyze the characteristic of the PSF and its influence on the deconvolution result.

    The PSF can be evaluated as:

    Psf(x,y)ej2π[(M1)RΔθλR0x+(N1)RΔfR0cy]sinc(2MRΔθλR0x)sinc(2NRΔfR0cy)

    (10)

    We can calculate the 2D mainlobe width which represents the radar imaging resolution as follows:

    ρx=λR02MRΔθ,ρy=R0c2NRΔf (11)

    Eq. (9) indicates that the MF result can be seen as the convolution result of backscatter coefficients and Psf(x,y). The PSF is characterized by synthetic aperture and bandwidth, which has strong low pass characteristic with low resolution and high sidelobes as shown in Eq. (10) and Eq. (11). For an isolated target scatter, imaging result after the MF output will be proportional to the PSF, therefore the resolution of MF result is limited and accompanied by low resolution and high sidelobes. Inspired by above, we can restore the high resolution backscatter coefficients information by deconvolution to remove the effect of low pass characteristic of PSF.

    As we have get 2D convolution form as Eq. (9), here we consider to use the direct deconvolution algorithm to recovery target backscatter coefficients. Firstly, we transform Eq. (9) into the spatial frequency domain using 2D Fourier transform as:

    Yω=ΣωHωω+Wω (12)

    where, Yω=F{YMF},Σω=F{˜Σ},Hω=F{Psf},Wω=F{WMF}.

    Theoretically, the target scattering information could be restored by deconvolution as:

    Σω=Yω/Hω (13)

    However, 1/Hω will be very large in practice at the outside of the mainlobe of PSF since the low pass characteristic of the PSF, which results in tremendous amplification of noise and obtains valueless results. So the deconvolution processing becomes an ill-posed inverse problem.

    In order to alleviate the ill-posed problem, we use Winner filtering algorithm and sparse low rank matrix recovery to improve the quality of imaging result.

    The result after Winner filter algorithm can be written as[14]:

    ˜Σω=YωHωHω2+ΨWW(ω)/ΨΣΣ(ω) (14)

    where ΨWW(ω) and ΨΣΣ(ω) is the power spectral density of W and Σ. Eq. (14) will approach Eq. (13) when the SNR is relatively high. What’s more, Eq. (14) will attenuate the high frequencies noise to alleviate the ill-posed problem under low SNR. In experimental data processing, the ΨWW(ω)/ΨΣΣ(ω) is generally set according to the experience value. We can get the scattering reflectivity ˜Σ by 2D Inverse Fourier transform according to Eq. (14).

    We can prove that the echo matrix after MF is sparse and low rank in Appendix A and by using this characteristic, the echo SNR can be improved. Consider the problem of estimating a sparse low rank matrix X from its noisy observation Y:

    Y=X+W (15)

    Define the sparse low rank matrix recovery problem as:

    min (16)

    where \gamma is the regularization parameter used to balance the relative contribution between nuclear norm and the 1-norm, which can control the denoising performance. In general, the denoising threshold of \gamma can be set as the 5%~10% of the maximum singular value of Y.

    By applying Augmented Lagrangian Method (ALM), we can get the optimization problem:

    \begin{align} F\left( {{{\text{X}}},{{\text{D}}},{{{\text{Y}}}_{\!1}},{{{\text{Y}}}_{\!2}},\mu } \right) \!=\! & \gamma {\left\| {{\text{X}}} \right\|_*} + \left\langle {{{{\text{Y}}}_{\!1}},{{\text{Y}}} - {{\text{X}}}} \right\rangle \\ &+\! \frac{\mu }{2}\left\| {{{\text{Y}}} \!-\! {{\text{X}}}} \right\|_F^2 \!+\! \left( {1 \!-\!\! \gamma } \right){\left\| {{\text{D}}} \right\|_1} \\ &+\! \left\langle {{{{\text{Y}}}_2},{{\text{D}}} - {{\text{X}}}} \right\rangle \!\!+\!\! \frac{\mu }{2}\left\| {{{\text{D}}} \!\!-\!\! {{\text{X}}}} \right\|_F^2 \end{align}

    (17)

    And the update rules for solving this problem are as follows:

    {{{\text{X}}}^{\left( {k + 1} \right)}} = \mathcal{S}\left( {\frac{{{{\text{Y}}} \!\!+\!\! {{{\text{D}}}^{\left( k \right)}}}}{2} \!+\! \frac{{{{\text{Y}}}_1^{\left( k \right)} \!+\! {{\text{Y}}}_2^{\left( k \right)}}}{{2{\mu ^{\left( k \right)}}}},\frac{\gamma }{{2{\mu ^{\left( k \right)}}}}} \right) (18)
    {{{\text{D}}}^{\left( {k + 1} \right)}} = {\rm{soft}}\left( {\frac{1}{{{\mu ^{\left( k \right)}}}}{{\text{Y}}}_2^{\left( k \right)} - {{{\text{X}}}^{\left( {k + 1} \right)}},\frac{{1 - \gamma }}{{{\mu ^{\left( k \right)}}}}} \right)\quad (19)
    \!\!\!\!\!\!\!\!\!\!\left. \begin{align} {{\text{Y}}}_1^{\left( {k + 1} \right)} =& {{\text{Y}}}_1^{\left( k \right)} + {\mu ^{\left( k \right)}}\left( {{{\text{Y}}} - {{{\text{X}}}^{\left( {k + 1} \right)}}} \right) \\ {{\text{Y}}}_2^{\left( {k + 1} \right)} =& {{\text{Y}}}_2^{\left( k \right)} + {\mu ^{\left( k \right)}}\left( {{{{\text{D}}}^{\left( {k + 1} \right)}} - {{{\text{X}}}^{\left( {k + 1} \right)}}} \right) \quad\; \\ {\mu ^{\left( {k + 1} \right)}} =& \beta {\mu ^{\left( k \right)}},\,\,\, \beta > 1 \end{align}\!\!\!\!\!\!\right\} (20)

    where, \mathcal{S}\left( { \cdot , \cdot } \right) is the singular value thresholding function defined as:

    \mathcal{S}\left( {{{\text{X}}},\gamma } \right){\rm{ = }}{{\text{U}}}{\rm{soft}}\left( {{\text{Σ}},\gamma } \right){{{\text{V}}}^{\rm T}} (21)

    where, {{\text{X}}} = {{\text{U}}}{\text{Σ}}{{{\text{V}}}^{\rm T}\,} is the Singular Value Decomposition (SVD) of {{\text{X}}}, {\rm{soft}}\left( \cdot \right) is the soft thresholding function defined as:

    {\rm{soft}}\left( {x,\gamma } \right) = {\rm{sign}}\left( x \right) \cdot \max \left\{ {\left| x \right| - \gamma ,0} \right\} (22)

    See Appendix B for the detailed derivation of Eq. (18) and Eq. (19).

    The flowchart of the proposed method is shown in Fig. 2 by combining the sparse low rank matrix recovery with the 2D deconvolution.

    Figure  2.  The flowchart of the proposed method

    The parameters in the simulation are given in Tab. 1. In this experiment, we set four-point targets, the imaging results are shown in Fig. 3.

    Table  1.  Simulation parameters
    ParameterValueParameterValue
    {{M}}256R1 m
    {{N}}500H0.7 m
    \Delta f10 MHzW0.04 m
    \Delta \theta 0.009°SNR–15 dB
     | Show Table
    DownLoad: CSV
    Figure  3.  Imaging results

    As shown in Fig. 3(a), due to the limitation of synthetic aperture and bandwidth, the MF method suffers from relatively low resolution and high sidelobes which make it difficult to distinguish between four-point targets even there is no noise. Fig. 3(b)Fig. 3(d) show the imaging results reconstructed by MF and proposed method including the intermediate denoising results when SNR = –15 dB. It can be clearly seen that the effect of denoising compared Fig. 3(c) with Fig. 3(a) and Fig. 3(b), the echo SNR is further improved by the sparsity and low rank matrix recovery during the proposed intermediate denoising procedure. The final imaging result is shown in Fig. 3(d), from which we can see that the proposed method has a better reconstruction precision with higher resolution imaging of four distinguishable point targets.

    The experimental scene is shown in Fig. 4(a), which is the same with the model in Fig. 1. The radar system consists of a pair of horn antennas, a turntable whose rotation angle can be precisely controlled by the computer, and an Agilent VNA N5224A which is used for transmitting and receiving the stepped-frequency signal with bandwidth of 10 GHz from 28 GHz to 38 GHz and number of frequencies N equals to 256 (Frequency interval \Delta f is 40 MHz). Two kind of targets including three 5-mm-diameter mental spheres and a pair of scissors placed on a rotatory platform are used here as shown in Fig. 4(b).

    Figure  4.  Experimental scene VNA

    As we know, image entropy can be considered as a metric for measuring the smoothness of the probability density function of image intensities[15]. The imaging entropy is defined as:

    E\left( I \right) = - \sum\limits_{p = 1}^P {\sum\limits_{q = 1}^Q {\left| {\frac{{{I^2}\left( {p,q} \right)}}{{s\left( I \right)}}} \right| \ln \left| {\frac{{{I^2}\left( {p,q} \right)}}{{s\left( I \right)}}} \right|} } (23)

    where s\left( I \right) = \displaystyle\sum\nolimits_{p = 1}^P {\sum\nolimits_{q = 1}^Q {{{\left| {I\left( {p,q} \right)} \right|}^2}} } .

    In this experiment, we set R \!=\! 1\;{\rm m},\ H \!=\! 0.7\;{\rm m},W = 0.04\;{\rm m} and the total rotating angle is {5^ \circ } with an angle interval \Delta \theta = {0.01^ \circ } (M = 500).

    Fig. 5 shows the results of the MF and our proposed method for the mental spheres. The one-dimensional x and y domain cross-section of the target with red-dashed circle shown in Fig. 5 are presented in Fig. 6, in which the red-dashed line and blue line represent the result of MF and proposed method. Clearly, the reconstruction result of proposed method has a narrower main-lobe and lower side-lobe than MF and the sharpening ratio almost reach 5.8 and 3.0 in x and y domain, respectively.

    Figure  5.  Imaging results of mental spheres
    Figure  6.  One-dimensional cut through the target with red dashed circle in Fig. 5

    The parameters for this experiment are set as follows, R = 0.876\;{\rm m}, W = 0.04\;{\rm m}. The total rotating angle is {360^ \circ } with M equals to {720^ \circ }. Taking into account the scintillation characteristics of the target under large rotating angle, we divide the rotating angle into 72 segments and each of the part is with rotating angle from {0^ \circ } to {5^ \circ }. The proposed method is used to process the data for each segment and the image fusion method is used to merge the results of all segments.

    Fig. 7 shows the imaging results of the scissors reconstructed by MF and proposed method. It can be seen from the results that the proposed method has a high reconstruction precision with a shaper shape of scissors.

    Figure  7.  Imaging results of scissors

    The entropies of the imaging results by MF and our proposed method are given in Tab. 2 to quantitatively assess the performance. The proposed method has a low entropy which means the proposed method can improve the resolution and verifies its superiority.

    Table  2.  Entropies of imaging results
    TargetMFOur proposed method
    Mental spheres8.72824.8429
    Scissors8.94337.0454
     | Show Table
    DownLoad: CSV

    We introduce a robust deconvolution method with enhancing SNR technology to realize high resolution radar imaging. Compared to other high resolution methods, our proposed method is simple and robust. Although the signal model and experiments are performed for turntable radar situation with SF waveform, the method can be directly generalized to other practical radar systems based on other types of signals.

    Appendix A Proof of the sparsity and low rank characteristic

    To prove the echo matrix after MF is sparse and low rank, the following lemma is needed.

    Lemma 1[16]: For matrix A and B, the ranks of the product of A and B satisfy the inequality below:

    {\rm{rank}}\left( {{{\text{AB}}}} \right) \le \min \left\{ {{\rm{rank}}\left( {{\text{A}}} \right),{\rm{rank}}\left( {{\text{B}}} \right)} \right\} (A-1)

    From Eq. (5) and Eq. (6), we can see that the echo matrix after MF can be written as:

    {{{\text{Y}}}_{\!\!{\rm {MF}}}} \ = {{\text{A}}}_x^{\rm H}{{{\text{A}}}_x}\tilde{{\text{Σ}} }{{\text{A}}}_y^{\rm T}{{\text{A}}}_y^* (A-2)

    We have supposed that the target has sparse distribution, so the target backscatter coefficients matrix \tilde{{\text{Σ}} } is sparse and low rank. Thus, matrix {{{\text{Y}}}_{{\!\rm{MF}}}} is also low rank according to lemma 1. The sparsity of matrix {{{\text{Y}}}_{{\rm{\!\!MF}}}} can be proved by Eq. (9) obviously.

    Appendix B Derivation of Eq. (18) and Eq. (19)

    For Eq. (18), the optimization problem can be described as Eq. (B-1), and it has a closed-form solution just as Eq. (18) according to Ref. [13].

    \begin{align} {{{\text{X}}}^{\left( {k + 1} \right)}}\!\!\!\!\!\!\!\!\!\!\! & \\ & =\!\! \arg \mathop {\min }\limits_{{\text{X}}} F\left( {{{\text{X}}},{{{\text{D}}}^{\left( k \right)}},{{\text{Y}}}_1^{\left( k \right)},{{\text{Y}}}_2^{\left( k \right)},{\mu ^{\left( k \right)}}} \right) \\ & =\!\! \arg \mathop {\min }\limits_{{\text{X}}} \frac{1}{2}\left\| {{\text{X}}} \!- \! \frac{1}{2}\left( {{{\text{Y}}} \!+\! {{{\text{D}}}^{\left( k \right)}} \!+\! \frac{{\text{Y}}_1^{\left( k \right)}\!+\!\! {{\text{Y}}}_2^{\left( k \right)}}{{{\mu ^{\left( k \right)}}}}} \right) \right\|_F^2 \\ &\quad + \frac{\gamma }{{2{\mu ^{\left( k \right)}}}}{\left\| {{\text{X}}} \right\|_*} \end{align} (B-1)

    For Eq. (19), it is the same with Eq. (18), which can written as

    \begin{align} {{{\text{D}}}^{\left( {k + 1} \right)}} = & \arg \mathop {\min }\limits_{{\text{D}}} F\left( {{{{\text{X}}}^{\left( {k + 1} \right)}},{{\text{D}}},{{\text{Y}}}_1^{\left( k \right)},{{\text{Y}}}_2^{\left( k \right)},{\mu ^{\left( k \right)}}} \right) \\ = & \arg \mathop {\min }\limits_{{\text{D}}} \frac{1}{2}\left\| {{{\text{D}}} - \left( {\frac{{{{\text{Y}}}_2^{\left( k \right)}}}{{{\mu ^{\left( k \right)}}}} - {{{\text{X}}}^{\left( {k + 1} \right)}}} \right)} \right\|_F^2 \\ &+ \frac{{1 - \gamma }}{{{\mu ^{\left( k \right)}}}}{\left\| {{\text{D}}} \right\|_1} \end{align} (B-2)

    It also has a closed-form solution as Eq. (19) according to Ref. [17].

  • 图  1  神经网络应用方法

    Figure  1.  Application methods of neural network

    图  2  拓扑构型FSS[23]

    Figure  2.  Topological FSS[23]

    图  3  优化后的带通型FSS[23]

    Figure  3.  The optimized bandpass FSS[23]

    图  4  宽带吸波复合材料拓扑构型[23]

    Figure  4.  Schematic illustration of wideband absorber topology configuration[23]

    图  5  吸收率随极化方式和入射角变化[23]

    Figure  5.  Variation of absorptivity with polarization mode and incident angle[23]

    图  6  基于拓扑优化设计的超表面吸波体

    Figure  6.  Metaurface absorber based on topology optimization design

    图  7  对称性破缺超表面吸波体结构示意图[29]

    Figure  7.  Structure illustration of the symmetry broken metasurface absorber[29]

    图  8  金属板与超表面的对比[29]

    Figure  8.  Comparisons between metal plate and metasurfaces[29]

    图  9  低RCS编码超表面[30]

    Figure  9.  Low RCS coded metasurface[30]

    图  10  RCS远场图[30]

    Figure  10.  RCS far-fields[30]

    图  11  拓扑结构单元[23]

    Figure  11.  Topology unit[23]

    图  12  编码超表面[23]

    Figure  12.  Coded metasurface[23]

    图  13  超表面样品[23]

    Figure  13.  Metasurface sample[23]

    图  14  基于遗传算法的优化流程示意图[37]

    Figure  14.  Optimization flow illustration based on genetic algorithm[37]

    图  15  曲线与金属线形[37]

    Figure  15.  Curve and metal line shape[37]

    图  16  吸波体的电磁响应[37]

    Figure  16.  Electromagnetic response of the absorber[37]

    图  17  Hopfield网络[41]

    Figure  17.  Hopfield network[41]

    图  18  Hopfield网络的联想记忆功能[41]

    Figure  18.  Associative memory function of Hopfield network[41]

    图  19  异常反射超表面[41]

    Figure  19.  Abnormal reflection metasurface[41]

    图  20  基于深度学习-遗传算法复合优化流程图[48]

    Figure  20.  Flow chart of composite optimization based on deep learning and genetic algorithm[48]

    图  21  Inception V3网络结构[46]

    Figure  21.  Network structure of Inception V3[46]

    图  22  超表面电磁性能[46]

    Figure  22.  Electromagnetic performances of metasureface[46]

    表  1  不同智能算法在超材料优化设计中优缺点对比

    Table  1.   Comparison of advantages and disadvantages of different intelligent algorithms in metamaterial optimization designs

    算法类型优点缺点
    启发式算法[912]全局搜索能力强,高度自定义,兼容性高计算速度慢,易陷入局部最优解
    机器学习[1316,2123]学习能力强,适应性好,领域优势明显训练成本高,硬件要求高,模型设计复杂
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