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Electrical Engineering and Systems Science > Signal Processing

arXiv:2210.05812 (eess)
[Submitted on 11 Oct 2022 (v1), last revised 27 Nov 2022 (this version, v3)]

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

Authors:Zahra Esmaeilbeig, Kumar Vijay Mishra, Arian Eamaz, Mojtaba Soltanalian
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Abstract: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.
Subjects: Signal Processing (eess.SP); Optimization and Control (math.OC)
Cite as: arXiv:2210.05812 [eess.SP]
  (or arXiv:2210.05812v3 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2210.05812
Related DOI: https://doi.org/10.1109/LSP.2022.3224681

Submission history

From: Zahra Esmaeilbeig [view email]
[v1] Tue, 11 Oct 2022 22:30:07 UTC (2,600 KB)
[v2] Tue, 22 Nov 2022 22:35:31 UTC (1,074 KB)
[v3] Sun, 27 Nov 2022 18:28:02 UTC (538 KB)
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