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Simulation study demonstrates improved tracking accuracy for extended objects using non-identical sensors, suggesting viable compensation for disparate measurement matrices.
Random-matrix based extended object tracking (EOT) has received much attention for the simple yet effective scheme. Multiple sensors can bring more information due to the different perspectives and characteristics especially for EOT. However, in practice, multiple sensors have different measurement matrices, which makes EOT even more challenging. This paper develops a variational Bayesian approach to multi-sensor EOT (VB-MS-EOT) based on random matrix. First, the system model of MS-EOT with different measurement matrices is developed. This model also considers the differently distorted observations and various measurement numbers of multiple sensors. To deal with the different measurement matrices and distortions, VB-MS-EOT is proposed, where the kinematic state and extension are estimated iteratively with a simple form. Compared with the traditional EOT based on random matrix using multiple sensors with identical measurement matrix, the effectiveness of VB-MS-EOT is illustrated by simulated data.
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Zhang et al. (2023) studied this question.
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