Emergency Localization for Mobile Ground Users: An Adaptive UAV Trajectory Planning Method

TL;DR

A single UAV localization method without hovering is designed, the maximum likelihood estimation (MLE) method is used, and the enhanced particle swarm optimization (EPSO) algorithm and edge access strategy are utilized to develop a low complexity localization-oriented adaptive trajectory planning algorithm.

Abstract

In emergency search and rescue scenarios, the quick location of trapped people is essential. However, disasters can render the Global Positioning System (GPS) unusable. Unmanned aerial vehicles (UAVs) with localization devices can serve as mobile anchors due to their agility and high line-of-sight (LoS) probability. Nonetheless, the number of available UAVs during the initial stages of disaster relief is limited, and innovative methods are needed to quickly plan UAV trajectories to locate non-uniformly distributed dynamic targets while ensuring localization accuracy. To address this challenge, we design a single UAV localization method without hovering, use the maximum likelihood estimation (MLE) method to estimate the location of mobile users and define the upper bound of the localization error by considering users' movement.Combining this localization method and localization error-index, we utilize the enhanced particle swarm optimization (EPSO) algorithm and edge access strategy to develop a low complexity localization-oriented adaptive trajectory planning algorithm. Simulation results demonstrate that our method outperforms other baseline algorithms, enabling faster localization without compromising localization accuracy.

Figures (4)

• Figure 1: Initial scan trajectory of 1 UAV in the disaster area
• Figure 2: Localization error calculation
• Figure 3: Trajectory of the UAV in the accurate localization stage
• Figure 4: Performance comparison in terms of task completion time, algorithm runtime and localization error