Particle Filters (PFs) have been successfully employed for monocular 3D model-based tracking of rigid objects. However, these filters depend on the computation of importance weighs that use sub-optimal approximations to the likelihood function. In this paper, we propose to enrich the filter with additional refinement steps to abridge its sub-optimality. We test the proposed approach in two different types of PFs: (i) an Unscented Particle Filter (UPF), and (ii) the recently proposed Unscented Bingham Filter (UBiF). These filters are applied to the outdoor tracking of a fixed-wing Unmanned Aerial Vehicle (UAV) autonomous landing in a Fast Patrol Boat (FPB), tested in a simulated environment with a real sky gradient filled with clouds. The use of the refinement steps significantly improves the overall accuracy of the method.

粒子滤波器 (PF) 已成功用于基于单目 3D 模型的刚性物体跟踪。然而，这些过滤器依赖于对似然函数使用次优近似的重要性权重的计算。在本文中，我们建议通过额外的细化步骤来丰富过滤器，以减少其次优性。我们在两种不同类型的 PF 中测试了所提出的方法：（i）无味粒子过滤器（UPF），以及（ii）最近提出的无味宾汉过滤器（UBiF）。这些过滤器应用于快速巡逻艇 (FPB) 中固定翼无人机 (UAV) 自主着陆的室外跟踪，在模拟环境中进行测试，真实天空梯度充满云层。细化步骤的使用显着提高了该方法的整体准确性。