Rotatable Antenna-Enhanced Wireless Sensing with Uniform Sparse Array via Tensor Decomposition

TL;DR

This paper introduces a novel wireless sensing system with a rotatable antenna array and a tensor decomposition-based DOA estimation algorithm, significantly improving sensing accuracy and unambiguity over traditional methods.

Contribution

It presents a new RA array design combined with a tensor decomposition approach for enhanced DOA estimation in sparse arrays, addressing spatial undersampling issues.

Findings

Achieves high-precision DOA estimation with unambiguous results.

Outperforms conventional dense arrays and omnidirectional systems in simulations.

Utilizes tensor decomposition to effectively process signals across multiple rotations.

Abstract

In this letter, we propose a new wireless sensing system equipped with a rotatable antenna (RA) array to enhance the sensing performance of a uniform sparse array (USA). To tackle the severe spatial undersampling issues, we propose a novel tensor decomposition-based direction-of-arrival (DOA) estimation algorithm. Specifically, we introduce a synchronous multiple rotation pattern for active target probing such that the received signals across multiple rotations to capture the diverse spatial degree of freedoms. Subsequently, we mathematically formulate the received signals across successive rotations as a third-order tensor, and leverage the canonical polyadic decomposition to obtain the factor matrices incorporating the DOA of targets. By analyzing the extrema distribution laws of array steering vector correlation (SVC) and gain SVC of RAs, we propose to combine the array and gain factor matrices via the Kronecker product, which theoretically guarantees the unambiguous DOA estimation. Simulation results demonstrate that the proposed RA-enhanced tensor decomposition-based algorithm achieves high-precision and unambiguous sensing performance compared to conventional uniform dense arrays and omnidirectional antenna systems.