# Flight Control for UAV Loitering Over a Ground Target with Unknown Maneuver

## TL;DR

This paper develops a UAV flight control system for loitering over a moving ground target with unknown, stochastic maneuvers, utilizing a novel estimation and control framework validated through simulations.

## Contribution

It introduces a combined guidance and control approach with a Rao-Blackwellised particle filter for maneuver estimation under stochastic target behavior.

## Key findings

- The proposed controller effectively tracks moving targets with unknown maneuvers.
- The RBPF requires fewer particles for accurate estimation.
- Simulation results demonstrate the controller's robustness and effectiveness.

## Abstract

This paper proposes a flight controller for an unmanned aerial vehicle (UAV) to loiter over a ground moving target (GMT). We are concerned with the scenario that the stochastically time-varying maneuver of the GMT is unknown to the UAV, which renders it challenging to estimate the GMT's motion state. Assuming that the state of the GMT is available, we first design a discrete-time Lyapunov vector field for the loitering guidance and then design a discrete-time integral sliding mode control (ISMC) to track the guidance commands. By modeling the maneuver process as a finite-state Markov chain, we propose a Rao-Blackwellised particle filter (RBPF), which only requires a few number of particles, to simultaneously estimate the motion state and the maneuver of the GMT with a camera or radar sensor. Then, we apply the principle of certainty equivalence to the ISMC and obtain the flight controller for completing the loitering task. Finally, the effectiveness and advantages of our controller are validated via simulations.