Cao Zhanglong, Bryant David, Molteno Timothy C A, Fox Colin, Parry Matthew
SAGI West, School of Molecular and Life Sciences, Curtin University, Perth 6085, Australia.
Department of Mathematics & Statistics, University of Otago, Dunedin 9054, New Zealand.
Sensors (Basel). 2021 May 6;21(9):3215. doi: 10.3390/s21093215.
Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline-which we name the V-spline-that incorporates position and velocity information and a penalty term that controls acceleration. We introduce an adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is proposed, and, in simulation studies, the V-spline shows superior performance to existing methods. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle.
轨迹重建是在连续观测之间推断移动物体路径的过程。在本文中,我们提出了一种平滑样条——我们称之为V样条——它结合了位置和速度信息以及一个控制加速度的惩罚项。我们引入了一种自适应V样条,旨在控制不规则采样观测和有噪声速度测量的影响。提出了一种用于估计V样条参数的交叉验证方案,并且在模拟研究中,V样条表现出优于现有方法的性能。最后,给出了V样条在二维车辆轨迹重建中的应用,其中惩罚项被允许进一步依赖于车辆的已知运行特性。