基于Bingham分布的方位递归估计
Recursive Estimation of Orientation Based on the Bingham Distribution
AI总结：
本文提出基于Bingham分布的二维递归滤波器，用于解决具有180度对称性的圆形滤波问题，适用于实时应用，并可扩展至四元数以跟踪三维方位。
摘要：
方向估计是许多跟踪应用中的常见问题。传统滤波器如卡尔曼滤波因未考虑问题的周期性而表现不佳。本文提出一种基于Bingham分布的递归滤波器，用于处理二维方向数据。所提滤波器适用于具有180度对称性的圆形滤波问题，即180度旋转无法区分。它易于使用标准数值技术实现，适用于实时应用。所提方法可扩展至四元数，从而允许跟踪任意三维方位。我们在具有挑战性的场景中评估了所提滤波器，并将其与传统卡尔曼滤波方法进行了比较。
英文摘要：
Directional estimation is a common problem in many tracking applications. Traditional filters such as the Kalman filter perform poorly because they fail to take the periodic nature of the problem into account. We present a recursive filter for directional data based on the Bingham distribution in two dimensions. The proposed filter can be applied to circular filtering problems with 180 degree symmetry, i.e., rotations by 180 degrees cannot be distinguished. It is easily implemented using standard numerical techniques and suitable for real-time applications. The presented approach is extensible to quaternions, which allow tracking arbitrary three-dimensional orientations. We evaluate our filter in a challenging scenario and compare it to a traditional Kalman filtering approach.
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