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Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking

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  • Xiaoke Lu
  • Zhiguo Zhang
  • Qing Li
  • Jinping Sun

Abstract

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.

Suggested Citation

  • Xiaoke Lu & Zhiguo Zhang & Qing Li & Jinping Sun, 2021. "Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, May.
    • Handle: RePEc:hin:jnlmpe:9942365
    DOI: 10.1155/2021/9942365
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