Dynamic Bearing–Angle for Vision-Based UAV Target Motion Analysis

TL;DR

This paper introduces a new algorithm called Dynamic Bearing–Angle to improve the accuracy and robustness of vision-based motion estimation for UAVs in dynamic environments.

Contribution

The paper proposes a dual robustness mechanism using dynamic noise adaptation and outlier suppression for improved target motion analysis.

Findings

The Dynamic Bearing–Angle algorithm effectively handles non-Gaussian noise and sudden target maneuvers.

Validation with simulations and real data shows the algorithm maintains robustness and accuracy under varying noise conditions.

Abstract

The Bearing–Angle algorithm effectively improves the observability of vision-based motion estimation for moving targets by combining the dimensional information of target detection frames. However, the robustness of this algorithm will be significantly reduced when the observation error increases due to sudden changes in the target motion state. To address this shortcoming, this paper proposes a visual target motion estimation algorithm called the Dynamic Bearing–Angle, which aims to improve the accuracy and robustness of target motion analysis in dynamic scenarios such as unmanned aerial vehicle (UAV). The algorithm innovatively introduces a dual robustness mechanism of dynamic noise intensity adaptation and outlier suppression based on M-estimation. By adjusting the noise covariance matrix in real time and assigning low weights to the outlier observations using the Huber weight function, the Dynamic Bearing–Angle algorithm is able to effectively cope with non-Gaussian noise and sudden target maneuvers. We validate the performance of the proposed algorithm with numerical simulations and real sensor data, and the results show that the Dynamic Bearing–Angle maintains good robustness and accuracy under different noise intensities.

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