Ground moving target tracking filter considering terrain and kinematics

Abstract

This paper presents a particle filter that considers the constraint that ground targets are constrained to the terrain surface, namely ‘terrain constraint’, to improve the tracking performance. Tracking of a ground target with terrain constraint is formulated as a constrained state estimation problem. Especially, not only the position constraint but also the velocity constraint is introduced in the formulation to make the joint constraint consistent with the kinematics. The ground-truth terrain elevation included in the terrain constraint is modeled with Gaussian process, and DTED is regarded as noisy observations of it. As a result, terrain constraint becomes a soft constraint that can reflect the uncertainty of DTED. Adding assumptions about the motion of the target, we propose a particle filter, STC-PF, to which the terrain constraint can be efficiently applied. STC-PF is based on SIR PF, but the major difference is that STC-PF uses the elevation model. Due to the elevation model, knowledge of the horizontal position and velocity of a target enables us to infer its vertical position and velocity more precisely. In a numerical simulation, STC-PF is compared with SCKF which can incorporate hard constraint only. Furthermore, to reflect the uncertainty in DTED, filters make use of DTED contaminated by noise whereas the ground-truth trajectory of the target is generated by the original DTED. The simulation result shows that STC-PF outperforms SCKF in terms of RMS error.