论文标题
CRAMER-RAO下限优化，用于通过多IR辅助雷达进行隐藏移动目标传感
Cramer-Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar
论文作者
论文摘要
智能反射表面（IRS）是一种快速新兴的范式，可实现非线（NLOS）无线传输。在本文中，我们重点介绍了移动的隐藏或NLOS目标的IRS辅助雷达估计性能。与使用单个IRS的先前作品不同，我们使用多个IRS平台研究了此问题，并通过得出相关的CRAMER-RAO下限（CRLB）来评估估计性能。然后，我们通过最小化关节参数CRLB矩阵的标量A型量度测量来设计多普勒感知的IRS相移。由此产生的优化问题是非凸，因此通过交替的优化框架来解决。数值结果表明，与非IRS和单人IRS替代方案相比，我们提出的优化相移的多个IRS平台的部署会导致更高的估计精度。
Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission. In this paper, we focus on IRS-aided radar estimation performance of a moving hidden or NLoS target. Unlike prior works that employ a single IRS, we investigate this problem using multiple IRS platforms and assess the estimation performance by deriving the associated Cramer-Rao lower bound (CRLB). We then design Doppler-aware IRS phase shifts by minimizing the scalar A-optimality measure of the joint parameter CRLB matrix. The resulting optimization problem is non-convex, and is thus tackled via an alternating optimization framework. Numerical results demonstrate that the deployment of multiple IRS platforms with our proposed optimized phase shifts leads to a higher estimation accuracy compared to non-IRS and single-IRS alternatives.
