Cooperative UAVs Placement Optimization for Best Multistatic Time-of-Arrival Localization in 5G Networks

Abstract

The fifth generation (5G) positioning, a breakthrough in cellular navigation, revolutionizes location services. Multistatic time-of-arrival (TOA) 5G localization is a topic of significant interest due to its exceptional performance benefits. The spatial arrangement of unmanned aerial vehicles (UAVs) and the positions of the targets play a crucial role in precisely determining the target’s location in 5G environments. This study introduces a novel approach to enhance multistatic 5G localization performance through the placement optimization of UAVs. The derivation of the Cramér-Rao lower bound (CRLB) for TOA-based multistatic 5G localization is given, using unit norm vectors instead of conventional trigonometric parameterizations. A distinctive dual iteration majorization-minimization (DIMM) algorithm is derived, grounded in the MM principle. Our method outperforms current state-of-the-art algorithms tailored for uncorrelated noise in measurements, as it effectively addresses both uncorrelated and correlated noise scenarios. Additionally, the proposed method outperforms both gradient descent and alternating directions method of multipliers (ADMM) approaches in terms of performance. A comprehensive analysis of computational complexity and convergence attests to the pragmatic viability of our methodology. Rigorous simulations affirm its effectiveness across diverse noise variances and UAV-target distances.