The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs

Abstract

Conflict detection is an essential issue in flying ad hoc networks (FANETs) to ensure the safety of unmanned aerial vehicles (UAVs) during flights. This paper assesses the applicability and utilization of a conflict detection algorithm that sees immediate trajectory as a straight line for short periods with nonlinear mobility models such as Gauss–Markov (GM). First, we use a straight line conflict detection algorithm with two nonlinear mobility models. Then, we perform an extensive simulation study to evaluate the performance. Additionally, we present a comprehensive discussion to tune the collision detection parameters efficiently. Simulation results indicate that an algorithm considering the immediate trajectory as a straight line to predict conflicts between UAVs can be applied with nonlinear mobilities and can provide an acceptable performance measured in false and missed alarms.

Keywords

• mobility model
• Gauss–Markov
• soft turn
• UAV
• drone
• FANET