An Adaptive Sliding Window Estimator for Positioning of Unmanned Aerial Vehicle Using a Single Anchor

TL;DR

This paper introduces an adaptive sliding window estimator for UAV positioning using a single anchor, combining multiple estimation techniques to improve accuracy and robustness in varying environments.

Contribution

The proposed estimator adaptively evaluates environmental effects and sensor reliability, integrating Kalman filtering and smoothing for enhanced UAV localization.

Findings

Achieves a root mean square error of 0.15 m, outperforming existing methods.

Demonstrates robustness in both standard and harsh environments.

Proven effective in real-world UAV control experiments.

Abstract

Localization using a single range anchor combined with onboard optical-inertial odometry offers a lightweight solution that provides multidimensional measurements for the positioning of unmanned aerial vehicles. Unfortunately, the performance of such lightweight sensors varies with the dynamic environment, and the fidelity of the dynamic model is also severely affected by environmental aerial flow. To address this challenge, we propose an adaptive sliding window estimator equipped with an estimation reliability evaluator, where the states, noise covariance matrices and aerial drag are estimated simultaneously. The aerial drag effects are first evaluated based on posterior states and covariance. Then, an augmented Kalman filter is designed to pre-process multidimensional measurements and inherit historical information. Subsequently, an inverse-Wishart smoother is employed to estimate posterior states and covariance matrices. To further suppress potential divergence, a reliability evaluator is devised to infer estimation errors. We further determine the fidelity of each sensor based on the error propagation. Extensive experiments are conducted in both standard and harsh environments, demonstrating the adaptability and robustness of the proposed method. The root mean square error reaches 0.15 m, outperforming the state-of-the-art approach. Real-world close-loop control experiments are additionally performed to verify the estimator's competence in practical application.