# An Unscented Kalman Filter Based on the Adams–Bashforth Method with Applications to the State Estimation of Osprey-Type Drones Composed of Tiltable Rotor Mechanisms

## TL;DR

This paper introduces a modified Unscented Kalman Filter using the Adams–Bashforth method to improve computational efficiency for state estimation in fast-moving drones.

## Contribution

A novel application of the Adams–Bashforth method to the Unscented Kalman Filter for efficient state estimation in nonlinear systems.

## Key findings

- The proposed method reduces computational load while maintaining high estimation accuracy.
- Simulation results show improved performance for state estimation in an Osprey-type drone model.
- The method is suitable for systems requiring high sampling frequencies.

## Abstract

In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state–space models, a method employing the Runge–Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for the state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams–Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model’s state estimation problem, followed by a comparison of estimation accuracy and computation time in state estimation simulations for the UAV model of an Osprey-type drone.