Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty

Unmanned aerial vehicles (UAVs) have garnered significant research interest across various fields due to their excellent maneuverability, scalability, and flexibility. However, potential collisions and other issues can disrupt communication and hinder functionality in real-world applications. Therefore, accurate localization of UAVs is crucial. Nonetheless, environmental factors and inherent stability issues can lead to node positional errors in UAV networks, compounded by inaccuracies in transmit power estimation, complicating the effectiveness of signal strength-based localization methods in achieving high accuracy. To mitigate the adverse effects of these issues, a novel received signal strength difference (RSSD)-based localization scheme based on a robust enhanced salp swarm algorithm (RESSA) is presented. In this algorithm, an elitism strategy based on tent opposition-based learning (TOL) is proposed to promote the leader to move around the food source. Differential evolution (DE) is then used to enhance the exploration ability of each agent and improve global search. In addition, a dynamic movement mechanism for followers is designed, enabling the swarm to swiftly converge towards the food source, thereby accelerating the overall convergence process. The RSSD-based Cramér–Rao lower bound (CRLB) with position uncertainty is derived to evaluate the performance. Experimental results are presented, which show that the proposed RESSA provides better localization performance than related methods in the literature.