Low-Complexity Three-Dimensional AOA-Cross Geometric Center Localization Methods via Multi-UAV network

TL;DR

This paper introduces two low-complexity 3-D AOA localization methods for UAV networks using multi-plane geometric centers, achieving near-optimal accuracy with reduced computational demands.

Contribution

It proposes two novel geometric center-based algorithms for 3-D UAV localization that are computationally efficient and approach the CRLB, improving practical applicability.

Findings

Methods approach the Cramer-Rao lower bound.

Proposed algorithms have lower complexity than MLE.

Effective in 3-D UAV positioning scenarios.

Abstract

Angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), it has higher accuracy and is not sensitive to time synchronization of the distributed sensors. However, there are few works focused on three-dimensional (3-D) scenario. Furthermore, although maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra high. It is hard to employ it in practical applications. This paper proposed two multiplane geometric center based methods for 3-D AOA in UAV positioning. The first method could estimate the source position and angle measurement noise at the same time by seeking a center of the inscribed sphere, called CIS. Firstly, every sensor could measure two angles, azimuth angle and elevation angle. Based on that, two planes are constructed. Then, the estimated values of source position and angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramer-Rao lower bound (CRLB) and have lower complexity than MLE.