Cram\'er-Rao Bound Optimization for Joint Radar-Communication Design

TL;DR

This paper introduces a joint radar-communication MIMO beamforming design optimizing the Cramér-Rao bound for target estimation, ensuring communication quality, and demonstrating significant performance improvements over existing methods.

Contribution

It develops a novel CRB-based optimization framework for joint radar and communication system design, including closed-form solutions and semidefinite relaxation techniques for multi-user scenarios.

Findings

Achieves globally optimal solutions via semidefinite relaxation.

Provides significant gains in target estimation accuracy.

Demonstrates effectiveness through numerical simulations.

Abstract

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks.