Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking
Mathematical Problems in Engineering
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Lu, X., Zhang, Z., Li, Q., & Sun, J. (2021). Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking. [Journal Article]. https://doi.org/10.1155/2021/9942365
<jats:p>This paper develops a robust extended-target multisensor multitarget multi-Bernoulli (ET-MS-MeMBer) filter for enhancing the unsatisfactory quality of measurement partitions arising in the classical ET-MS-MeMBer filter due to increased clutter intensities. Specifically, the proposed method considers the influence of the clutter measurement set by introducing the ratio of the target likelihood to the clutter likelihood. With the constraint of the clutter measurement set, it can obtain better multisensor measurement partitioning results under the original two-step greedy partitioning mechanism. Subsequently, the single-target multisensor likelihood function for the clutter case is derived. Simulation results reveal a favorable comparison to the ET-MS-MeMBer filter in terms of accuracy in estimating the target cardinality and target state under conditions with increased clutter intensities.</jats:p>
External DOI: https://doi.org/10.1155/2021/9942365
This record's DOI: https://doi.org/10.17863/CAM.70637
Rights Holder: Copyright © 2021 Xiaoke Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.