Cramer-Rao Bound Optimization for Joint Radar-Communication Beamforming

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramer-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 be generally 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.

Automated summary

This paper proposes a MIMO beamforming design for joint radar sensing and multi-user communications. By minimizing the Cramer-Rao bound (CRB) of radar sensing while guaranteeing a pre-defined SINR level for each communication user, the target estimation performance is improved. Closed form optimal solutions are derived for both point and extended targets in the single-user scenario. For the multi-user scenario, both problems are relaxed into semidefinite programming, and the global optimum can be generally obtained.