3-D Trajectory Optimization for Robust Direction Sensing in Movable Antenna Systems

TL;DR

This paper introduces a 3-D movable antenna system that optimizes trajectory to improve direction sensing accuracy and robustness across all angles, outperforming fixed and 2-D movement systems.

Contribution

It derives a closed-form MSAEB expression, analyzes 2-D versus 3-D movement performance, and develops a convex optimization algorithm for optimal 3-D antenna trajectories.

Findings

3-D movement achieves isotropic sensing performance.

Proposed scheme significantly reduces worst-case angular error.

Numerical results show improved robustness over traditional systems.

Abstract

This paper presents a novel wireless sensing system where a movable antenna (MA) continuously moves and receives sensing signals within a three-dimensional (3-D) region to enhance sensing performance compared with conventional fixed-position antenna (FPA)-based sensing. We show that the performance of direction vector estimation for a target is fundamentally related to the 3-D MA trajectory in terms of the mean square angular error lower-bound (MSAEB), which is adopted as a coordinate-invariant performance metric. In particular, the closed-form expression of the MSAEB is derived as a function of the trajectory covariance matrix. Theoretical analysis shows that two-dimensional (2-D) antenna movement suffers from performance divergence for target direction close to the endfire direction of the 2-D MA plane, whereas 3-D movement can achieve isotropic sensing performance over the entire angular region. To achieve robust sensing performance, we formulate a min-max optimization problem to minimize the maximum (worst-case) MSAEB over a given continuous angular region wherein the target is located. An efficient successive convex approximation (SCA) algorithm is developed to optimize the 3-D MA trajectory and obtain a locally optimal solution. Numerical results demonstrate that the proposed 3-D MA sensing scheme is able to significantly reduce the worst-case mean square angular error (MSAE) compared with conventional arrays with FPAs and MA systems with 2-D movement only, thus achieving more accurate and robust direction estimation over the entire angular region.