# Target Sensing with Intelligent Reflecting Surface: Architecture and Performance

arXiv: https://arxiv.org/abs/2201.09091 · HTML: https://mlsanity.com/paper/2201.09091/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach](https://mlsanity.com/paper/2308.13616/) (2023)
- [Low-Communication Resilient Distributed Estimation Algorithm Based on Memory Mechanism](https://mlsanity.com/paper/2508.02705/) (2025)
- [Anti-Jamming Sensing with Distributed Reconfigurable Intelligent Metasurface Antennas](https://mlsanity.com/paper/2508.04964/) (2025)
- [Adaptive Learning for IRS-Assisted Wireless Networks: Securing Opportunistic Communications Against Byzantine Eavesdroppers](https://mlsanity.com/paper/2508.08206/) (2025)
- [Geometry-Informed Optimization of Binary RIS Configurations for Communication and Sensing](https://mlsanity.com/paper/2608.04133/) (2026)

## Abstract (arXiv)

Intelligent reflecting surface (IRS) has emerged as a promising technology to reconfigure the radio propagation environment by dynamically controlling wireless signal's amplitude and/or phase via a large number of reflecting elements. In contrast to the vast literature on studying IRS's performance gains in wireless communications, we study in this paper a new application of IRS for sensing/localizing targets in wireless networks. Specifically, we propose a new self-sensing IRS architecture where the IRS controller is capable of transmitting probing signals that are not only directly reflected by the target (referred to as the direct echo link), but also consecutively reflected by the IRS and then the target (referred to as the IRS-reflected echo link). Moreover, dedicated sensors are installed at the IRS for receiving both the direct and IRS-reflected echo signals from the target, such that the IRS can sense the direction of its nearby target by applying a customized multiple signal classification (MUSIC) algorithm. However, since the angle estimation mean square error (MSE) by the MUSIC algorithm is intractable, we propose to optimize the IRS passive reflection for maximizing the average echo signals' total power at the IRS sensors and derive the resultant Cramer-Rao bound (CRB) of the angle estimation MSE. Last, numerical results are presented to show the effectiveness of the proposed new IRS sensing architecture and algorithm, as compared to other benchmark sensing systems/algorithms.