Enhanced Direction-Sensing Methods and Performance Analysis in Low-Altitude Wireless Network via a Rotating Antenna Array

TL;DR

This paper introduces a low-complexity, enhanced direction-sensing method for low-altitude wireless networks using a rotating antenna array, improving efficiency and accuracy over existing techniques.

Contribution

It proposes a novel pre-rotation initialization and iterative greedy spatial-spectrum search framework that reduces computational complexity and enhances sensing performance.

Findings

The proposed PRI-IGSS method outperforms RR-Root-MUSIC in simulations.

It achieves the Cramér-Rao Lower Bound (CRLB) in mean squared error.

The method significantly reduces the need for mechanical rotations and eigendecompositions.

Abstract

Due to the directive property of each antenna element, the received signal power can be severely attenuated when the emitter deviates from the array boresight, which will lead to a severe degradation in sensing performance along the corresponding direction. Although existing rotatable array sensing methods such as recursive rotation (RR-Root-MUSIC) can mitigate this issue by iteratively rotating and sensing, several mechanical rotations and repeated eigendecomposition operations are required to yield a high computational complexity and low time-efficiency. To address this problem, a pre-rotation initialization with recieve power as a rule is proposed to signifcantly reduce the computational complexity and improve the time-efficiency. Using this idea, a low-complexity enhanced direction-sensing framework with pre-rotation initialization and iterative greedy spatial-spectrum search (PRI-IGSS) is develped with three stages: (1) the normal vector of array is rotated to a set of candidates to find the opimal direction with the maximum sensing energy with the corresponding DOA value computed by the Root-MUSIC algorithm; (2) the array is mechanically rotated to the initial estimated direction and kept fixed; (3) an iterative greedy spatial-spectrum search or recieving beamforming method, moviated by reinforcement learning, is designed with a reduced search range and making a summation of all previous sampling variance matrices and the current one is adopted to provide an increasiong performance gain as the iteration process continues. To assess the performance of the proposed method, the corresponding CRLB is derived with a simplified rotation model. Simulation results demonstrate that the proposed PRI-IGSS method performs much better than RR-Root-MUSIC and achieves the CRLB in term of mean squared error due to the fact there is no sample accumulation for the latter.