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arXiv · 1803.07184

Adaptive Smoothing for Trajectory Reconstruction

Zhanglong Cao·David Bryant·Tim Molteno·Colin Fox·Matthew Parry
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Abstract

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 a particular 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 given and we detail the performance of the V-spline on four particularly challenging test datasets. 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.

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Zhanglong Cao, David Bryant, Tim Molteno, Colin Fox, Matthew Parry. 2018-03-19. Adaptive Smoothing for Trajectory Reconstruction. https://doi.org/10.3390/s21093215

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