Cramer-Rao Bounds for Target Parameter Estimation in a Bi-Static IRS-Assisted Radar Configuration

TL;DR

This paper derives the Cramer-Rao Bound for target parameter estimation in a bi-static IRS-assisted radar setup, providing insights into system parameter effects and serving as a benchmark for future techniques.

Contribution

It introduces a signal model and CRB analysis for IRS-assisted bi-static radar, a novel focus not previously explored in target parameter estimation.

Findings

CRB depends on SNR, snapshots, IRS elements, and weights

Simulation results illustrate parameter influence on estimation accuracy

Provides a benchmark for future estimation methods

Abstract

The use of Intelligent Reflective Surfaces (IRS) to assist communication and sensing has proven cost-effective in challenging scenarios. For sensing, IRS is shown to sense non-line-of-sight (NLOS) and stealth targets, albeit with significant loss due to the four-hop path model. Amongst the available IRS-assisted configurations, we consider a three-hop model in which the IRS redirects the scattered target response towards the mono-static radar. With the IRS spatially displaced from the radar, this configuration mimics a bi-static radar. While target detection has been studied in this configuration, parameter estimation has not been investigated to date. To this end, we first develop the signal model for this configuration and derive the CRB for target parameters. The dependence of CRB on system parameters such as SNR, number of snapshots, number of IRS elements and their weights is brought forward through extensive simulations. This study can enable a designer to customize the system parameters to meet the requirements. It also serves as a benchmark for parameter estimation techniques developed for this configuration.