Xiaohua Zhou, Xu Du, Yijie Mao
This paper focuses on radar waveform optimization for minimizing the Cramer-Rao bound (CRB) in a multiple-input multiple-output (MIMO) radar system. In contrast to conventional approaches relying on semi-definite programming (SDP) and optimization toolboxes like CVX, we introduce a pioneering and efficient waveform optimization approach in this paper. Our proposed algorithm first applies sequential linear approximation to transform the original CRB-based problem with the transmit power constraint into a sequence of convex subproblems. By introducing a proximal term and further leveraging the KarushKuhn-Tucker (KKT) conditions, we derive the optimal closedform solution for each subproblem. The convergence of the proposed algorithm is then proved rigorously. Numerical results demonstrate that the proposed approach significantly reduces computational complexity - at least two orders of magnitude lower than the baseline algorithms while maintaining the same radar sensing accuracy.
https://arxiv.org/pdf/2409.12569
(Accepted by Globecom Workshop 2024)
@misc{zhou2024cramerraoboundbasedwaveform,
title={Cram'er-Rao Bound Based Waveform Optimization for MIMO Radar: An Efficient Linear-Proximal Method},
author={Xiaohua Zhou and Xu Du and Yijie Mao},
year={2024},
eprint={2409.12569},
archivePrefix={arXiv},
primaryClass={eess.SP},
url={https:arxiv.orgabs2409.12569}
}