Polyphase Waveform and Reflection Design Based on RIS-Aided Radar System
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摘要: 该文通过联合优化雷达发射波形，接收滤波器以及部署在场景中的智能反射面(RIS)，来增强雷达系统在杂波环境下的目标检测性能。在雷达波形和RIS相移矢量离散相位约束的前提下，该文采用系统输出信干噪比(SINR)为优化目标来建立RIS辅助下的雷达目标检测性能增强问题。为求解所形成的联合非凸优化问题，该文提出了一种交替优化求解策略，在每一轮迭代中基于优化子最大化的思想次序的两个关于波形和RIS相移矢量的优化子问题。仿真实验证明所提优化算法能够在满足恒模多相约束的情况下，提供高质量的RIS相移矢量-雷达收发波形，使得RIS辅助下的雷达系统目标检测性能得到明显的增强。Abstract: This study addresses the joint design problem of radar transmit waveform, receive filter and Reconfigurable Intelligent Surface (RIS) reflecting coefficients with an aim to enhance the target detection performance of a RIS-aided radar system. With restrictions on the finite phases of the radar waveform and RIS reflecting coefficients, the performance enhancement problem is formulated as a nonconvex joint design, in which the system Signal-to-Interference-plus-Noise-Ratio (SINR) is taken as the optimization objective. A novel cyclic optimization algorithm is developed to tackle this. In each cycle, two subproblems with respect to waveform and RIS reflecting coefficients are involved and are resolved by leveraging the Minorization Maximization (MM) strategy. The simulation results highlight the effectiveness of the proposed algorithm for providing high-quality RIS reflecting coefficients, radar transmit waveform, and receive filter.
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算法1 基于交替优化框架的RIS相移矢量-雷达波形联合优化算法 输入： 雷达到RIS各个阵元之间的距离$\left\{ {{\tau _{{{\rm{Rad}}},n}}} \right\}_{n = 1}^N$，目标(非
目标散射体)到RIS各个阵元之间的距离$\left\{ {{\tau _{{{\rm{Tar}}},n}}} \right\}_{n = 1}^N$
($\left\{ {{\tau _{{{\rm{Clu}}},k,n}}} \right\}_{n = 1}^N$)，雷达到目标(非目标散射体)之间的距离
${\tau _{{{\rm{Rad}},{\rm{Tar}}}}}$($ \left\{ {{\tau _{{\rm{Rad}},{\rm{Clu}},k}}} \right\}_{k = 1,n = 1}^{K,N} $)，信号无关干扰项目协方差矩阵
${{\boldsymbol{R}}_{{\rm{n}}}}$，杂波RCS$ \left\{ {{{\tilde \sigma }_{{{\rm{Clu}}},k}}} \right\}_{k = 1}^K $，RIS可用相位数$ {N_{{\rm{RIS}}}} $，波形可用
相位数$ {N_{{\rm{WF}}}} $，发射波形能量$ {e_t} $;输出：问题$ \mathcal{P} $的解$ \left( {{{\boldsymbol{w}}^ \star },{{\boldsymbol{s}}^ \star },{{\boldsymbol{\phi}} ^ \star }} \right) $; 1：初始化波形矢量$ {\boldsymbol{s}} $和相移矢量$ {\boldsymbol{\phi}} $的值; 2：迭代求解$ \left\{ {{\mathcal{P}_{{\boldsymbol{s}},\left( m \right)}}} \right\}_{k = 1}^{ + \infty } $以更新波形矢量$ {\boldsymbol{s}} $； 3：迭代求解$\left\{ { { {\ddot {\mathcal{P}} }_{\tilde {\boldsymbol{\phi}} ,\left( m \right)} } } \right\}_{m = 1}^{ + \infty }$，并根据$ {\phi _n} = {\tilde \phi _n}/{\tilde \phi _{N + 1}}, $
$ n = 1,2,\cdots,N $更新相移矢量$ {\boldsymbol{\phi}} $;4：重复步骤2—步骤3直到收敛，并记收敛处的波形矢量和RIS
相移矢量分别为$ {{\boldsymbol{s}}^ \star } $和$ {{\boldsymbol{\phi}} ^ \star } $;5：根据${{\boldsymbol{w}} = \alpha {{\boldsymbol{\varPsi}} ^{ - 1}}{{\boldsymbol Z}_{{\rm{Tar}}}}{\boldsymbol{s}}} $计算滤波器矢量$ {{\boldsymbol{w}}^ \star } $ 6：返回：问题$ \mathcal{P} $的解$ \left( {{{\boldsymbol{w}}^ \star },{{\boldsymbol{s}}^ \star },{{\boldsymbol{\phi }}^ \star }} \right) $; 
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