Spline Estimation of Paths Using Bearings-Only Tracking Data
提出用三次样条拟合移动物体平滑路径的最大似然估计方法，假设观测误差服从冯·米塞斯分布，适用于数据稀疏和异常值检测，并通过模拟和野生动物无线电追踪实例验证。
Abstract In many applications, bearings are measured to a moving object with the goal of estimating the object's course of movement. If movement is appropriately modeled as a smooth deterministic curve in the plane, then a cubic spline is a reasonable representation of the curve. Maximum likelihood estimators are presented for parameters of regression splines, assuming that observation errors follow a Von Mises distribution. Location estimates are obtainable even when data are sparse. Path estimation error, number and placement of knots, and outlier detection are discussed. Examples, including both simulated paths and observations from a wildlife radio-tracking study, are presented.