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Extended Object Tracking Using an Orientation Vector Based on Constrained Filtering
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OA
AI
DOI：10.3390/rs17081419.png)
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Abstract
En 中文
In many extended object tracking applications (e.g., tracking vehicles using a millimeter-wave radar), the shape of an extended object (EO) remains unchanged while the orientation angle varies over time. Thus, tracking the shape and the orientation angle as individual parameters is reasonable. Moreover, the tight coupling between the orientation angle and the heading angle contains information on improving estimation performance. Hence, this paper proposes a constrained filtering approach utilizing this information. First, an EO model is built using an orientation vector with a heading constraint. This constraint is formulated using the relation between the orientation vector and the velocity vector. Second, based on the proposed model, a variational Bayesian (VB) approach is proposed to estimate the kinematic, shape, and orientation vector states. A pseudo-measurement is constructed from the heading constraint and is incorporated into the VB framework. The proposed approach can also address the ambiguous issue in orientation angle estimation. Simulation and real-data results are presented to illustrate the effectiveness of the proposed model and estimation approach.
Keywords:
extended object tracking
constrained filtering
orientation vector
variational Bayesian