Cram\'er-Rao Bound Based Waveform Optimization for MIMO Radar: An Efficient Linear-Proximal Method

TL;DR

This paper presents an efficient linear-proximal algorithm for waveform optimization in MIMO radar systems that minimizes the Cramér-Rao bound, significantly reducing computational complexity compared to traditional SDP-based methods.

Contribution

Introduces a novel linear-proximal method for CRB-based waveform optimization that avoids SDP and offers faster convergence with maintained accuracy.

Findings

Reduces computational complexity by over 100 times

Maintains radar sensing accuracy equivalent to baseline methods

Proves convergence of the proposed algorithm

Abstract

This paper focuses on radar waveform optimization for minimizing the Cram\'er-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 Karush-Kuhn-Tucker (KKT) conditions, we derive the optimal closed-form 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.