Abstract
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.
Abstract (translated)
本文介绍了一个基于线性样条的二维扩展目标跟踪与分类框架。不同于最先进的模型，线性样条能够表示轮廓为任意复杂曲线的扩展对象。对于噪声测量可以来自扩展物体轮廓上任何点的情况，推导出一个精确的可能性；而对于散射点可以在任何地方（即内部或轮廓上的对象表面）的情况，则提供了一个近似的蒙特卡罗可能性。利用这种可能性来衡量观测数据与给定形状的吻合程度，开发了一种合适的估计器。所提出的估计器通过动力学状态模型扩展物体，提供了目标的位置和方向，以及一个描述物体轮廓和表面的形状向量。动力学状态是通过非线性卡尔曼滤波器进行估计的，而形状向量则是通过贝叶斯分类器来估计的，从而在形状估计过程中隐含地解决了分类问题。提供了一些数值实验以评估与最先进的扩展目标估计算法相比，所提出方法的有效性。
URL
https://arxiv.org/abs/2410.24183
