Error Analysis and Accuracy Improvement of Vision-Based Multi-UAV Cooperative Target Localization

Abstract

Vision-based cooperative target localization by multiple UAVs is an important part of the multi-UAVs for military and civilian missions. Compared with the common-used single UAV localization methods, cooperative localization is more adaptable since no predefinition of target states is required. However, it is difficult to analysis the effects of sensor noise and measurement error on the target localization error due to the complex nonlinear observation model. Therefore, this paper analyzes the localization error based on a Monte Carlo simulation method. To improve the localization accuracy, the optimal configuration is obtained. An unscented Kalman filter (UKF) based filtering method is used to further reduce the effect of sensor noise. Finally, a flight experiment is performed to verify the effectiveness of the proposed method in reducing the localization error.