# Cramer-Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar

arXiv: https://arxiv.org/abs/2210.05812 · HTML: https://mlsanity.com/paper/2210.05812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2022.

## Built on by (most cited first, 1 of 1)

- [Machine Learning for Metasurfaces Design and Their Applications](https://mlsanity.com/paper/2211.01296/) (2022)

## Abstract (arXiv)

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.