Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing

TL;DR

This paper explores the tradeoff between bistatic positioning and monostatic sensing in MIMO-OFDM systems, proposing optimization methods to enhance both objectives simultaneously.

Contribution

It introduces a multi-objective CRB-based optimization framework and mismatch-minimizing approaches for beamformer design in joint positioning and sensing.

Findings

Proposed methods improve the tradeoff performance.

Significant gains achieved with mismatch minimization.

Highlights advantages of weighted-sum mismatch minimization.

Abstract

We investigate the performance tradeoff between \textit{bistatic positioning (BP)} and \textit{monostatic sensing (MS)} in a multi-input multi-output orthogonal frequency division multiplexing scenario. We derive the Cram\'er-Rao bounds (CRBs) for BP at the user equipment and MS at the base station. To balance these objectives, we propose a multi-objective optimization framework that optimizes beamformers using a weighted-sum CRB approach, ensuring the weak Pareto boundary. We also introduce two mismatch-minimizing approaches, targeting beamformer mismatch and variance matrix mismatch, and solve them distinctly. Numerical results demonstrate the performance tradeoff between BP and MS, revealing significant gains with the proposed methods and highlighting the advantages of minimizing the weighted-sum mismatch of variance matrices.