Cramér–Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar
Cramér–Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar
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DOI：
10.1109/lsp.2022.3224681
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发表时间：
2022-10
影响因子：
3.9
通讯作者：
Zahra Esmaeilbeig;K. Mishra;Arian Eamaz;M. Soltanalian
中科院分区：
文献类型：
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作者：
Zahra Esmaeilbeig;K. Mishra;Arian Eamaz;M. Soltanalian
智能反射面(IRS)是一种能够实现非视距(NLOS)无线传输的迅速崛起的范例。本文主要研究IRS辅助雷达对运动隐蔽目标或非视距目标的估计性能。与以往采用单一IRS的工作不同，我们使用多个IRS平台来研究这一问题，并通过推导相关的Cramér-Rao下界(CRLB)来评估估计性能。然后，我们通过最小化联合参数CRLB矩阵的标量A-最优度来设计可感知多普勒的IRS相移。由此产生的优化问题是非凸的，因此通过交替的优化框架来处理。数值结果表明，与非IRS和单IRS方案相比，采用本文提出的优化相移方案部署多个IRS平台可以获得更高的估计精度。
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 Cramér-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.