# Performance Evaluation and Hybrid Application of the Greedy and Predictive UAV Trajectory Optimization Methods for Localizing a Target Mobile Device

## TL;DR

This paper compares greedy and predictive UAV trajectory optimization methods for localizing a target mobile device in realistic signal environments, addressing scenarios where GNSS data is unreliable or unavailable.

## Contribution

It provides a comparative analysis of the localization performance of two UAV trajectory strategies under realistic RSS error conditions, which was previously unexplored.

## Key findings

- Predictive approach outperforms greedy in high RSS error scenarios.
- Localization accuracy decreases as RSS error increases.
- The study highlights the importance of trajectory strategy choice in emergency localization.

## Abstract

This study investigates unmanned aerial vehicle (UAV) trajectory planning strategies for localizing a target mobile device in emergency situations. The global navigation satellite system (GNSS)-based accurate position information of a target mobile device in an emergency may not be always available to first responders. For example, 1) GNSS positioning accuracy may be degraded in harsh signal environments and 2) in countries where emergency positioning service is not mandatory, some mobile devices may not report their locations. Under the cases mentioned above, one way to find the target mobile device is to use UAVs. Dispatched UAVs may search the target directly on the emergency site by measuring the strength of the signal (e.g., LTE wireless communication signal) from the target mobile device. To accurately localize the target mobile device in the shortest time possible, UAVs should fly in the most efficient way possible. The two popular trajectory optimization strategies of UAVs are greedy and predictive approaches. However, the research on localization performances of the two approaches has been evaluated only under favorable settings (i.e., under good UAV geometries and small received signal strength (RSS) errors); more realistic scenarios still remain unexplored. In this study, we compare the localization performance of the greedy and predictive approaches under realistic RSS errors (i.e., up to 6 dB according to the ITU-R channel model).