Movable antennas improve near-field target location accuracy
Antenna Placement for Movable Antenna-Enabled Near-Field Target Localization
Information Theory
Summary
Finding an object's exact location using wireless signals can be tricky when the antennas are fixed in place. The authors studied how moving antennas around can help get a more accurate position of a target that sends signals nearby. They developed a mathematical way to decide the best spots to place these antennas to minimize errors. Their method groups antennas into three main clusters and performs better than traditional fixed antenna setups. This could help improve technologies that rely on figuring out where things are based on wireless signals.
What this means in practice
- •For wireless device engineers: Design wireless receivers that adjust antenna placement dynamically to improve positioning accuracy of nearby signal sources.
- •For robotics system builders: Implement movable antenna clusters on robots to enhance real-time localization of signal-emitting objects nearby.
Authors
Jinjian Liu, Xianxin Song, Xianghao Yu
Abstract
Movable antennas (MAs) have recently emerged as a promising technology to enhance the wireless sensing performance by exploiting additional spatial degrees of freedom. In this work, we consider an MA-enabled near-field target localization problem under a device-based sensing architecture, where the sensing target actively transmits signals and the sensing receiver estimates its location based on the received signals. We adopt the squared position error bound (SPEB) as the sensing performance metric and aim to minimize the target's SPEB by optimizing the antenna placement at the sensing receiver. The resulting optimization problem is highly non-convex due to the minimum antenna spacing constraints and the intricate dependence of the SPEB on the antenna locations and the intractable form of SPEB. To tackle this problem, we first relax the inter-spacing constraints and accordingly formulate an antenna position distribution problem in terms of degree-4 truncated moments. The reformulated moment-based problem admits an equivalent semidefinite programming formulation and can be optimally solved using standard convex optimization solvers. Using truncated moment theory, we then recover an optimal antenna position distribution supported on a finite number of locations and construct an efficient clustered placement policy. Numerical results demonstrate that the constructed optimal distribution exhibits a three-cluster structure with source-dependent weights. The proposed antenna placement method outperforms conventional array geometries and closely approaches the exhaustive-search benchmark scheme.