用线性样条建模复杂形状目标，实现高精度跟踪与分类
Extended Object Tracking and Classification based on Linear Splines
- 用线性样条精确表示任意复杂轮廓的扩展目标
- 提出精确似然与蒙特卡洛近似似然，支持散点在轮廓或内部的观测
- 结合非线性卡尔曼滤波与贝叶斯分类器，边估计形状边完成分类
本文提出一种基于线性样条的二维扩展目标跟踪与分类框架。与现有方法不同，线性样条可表征轮廓为任意复杂曲线的扩展目标。针对测量点可能分布于目标轮廓任意位置的情况，推导出精确似然；对于测量点位于目标表面（包括内部或轮廓）的情况，提供近似的蒙特卡洛似然。利用该似然评估观测数据与给定形状的匹配程度，设计相应估计器。该估计器将扩展目标建模为包含运动状态（位置、朝向）与形状向量（刻画轮廓与表面）的组合。运动状态通过非线性卡尔曼滤波估计，形状向量则通过贝叶斯分类器估计，使分类问题在形状估计过程中隐式解决。通过数值实验，对比现有先进扩展目标估计算法，验证了所提方法的有效性。
原文摘要 · Abstract (English)
This paper introduces a framework based on linear splines for 2-dimensional extended object tracking and classification. Unlike state of the art models, linear splines allow to represent extended objects whose contour is an arbitrarily complex curve. An exact likelihood is derived for the case in which noisy measurements can be scattered from any point on the contour of the extended object, while an approximate Monte Carlo likelihood is provided for the case wherein scattering points can be anywhere, i.e. inside or on the contour, on the object surface. Exploiting such likelihood to measure how well the observed data fit a given shape, a suitable estimator is developed. The proposed estimator models the extended object in terms of a kinematic state, providing object position and orientation, along with a shape vector, characterizing object contour and surface. The kinematic state is estimated via a nonlinear Kalman filter, while the shape vector is estimated via a Bayesian classifier so that classification is implicitly solved during shape estimation. Numerical experiments are provided to assess, compared to state of the art extended object estimators, the effectiveness of the proposed one.
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