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Measurement-Driven Multi-Target Multi-Bernoulli Filter

机译：测量驱动的多目标多伯努利滤波器

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摘要

A measurement-driven multi-target multi-Bernoulli (MeMBer) filter which modifies the MeMBer filter by the measurements information is proposed in this paper. The proposed filter refines both the legacy estimates and the data-induced estimates of the MeMBer filter. For the targets under the legacy track set, the detection probabilities derived from the measurements are employed to refine the multi-target distribution. And for the targets under the data-induced track set, the multi-target distribution is further improved by the modified existence probabilities of the legacy tracks. Unlike the cardinality balanced MeMBer (CBMeMBer) filter, the proposed filter removes the cardinality bias in the MeMBer filter by utilizing the measurements information. Simulation results show that, compared with the traditional methods, the proposed filter can improve the stability and accuracy of the estimates and does not need the high detection probability hypothesis.
机译：本文提出了一种测量驱动的多目标多伯努利（MeMBer）滤波器，该滤波器通过测量信息来修改MeMBer滤波器。所提出的过滤器改进了MeMBer过滤器的传统估计和数据诱发的估计。对于传统轨迹集下的目标，采用从测量得出的检测概率来完善多目标分布。对于数据诱导的轨迹集下的目标，通过修改传统轨迹的存在概率进一步改善了多目标分布。与基数平衡的MeMBer（CBMeMBer）滤波器不同，建议的滤波器通过利用测量信息来消除MeMBer滤波器中的基数偏差。仿真结果表明，与传统方法相比，该滤波器可以提高估计的稳定性和准确性，不需要较高的检测概率假设。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第9期|6515608.1-6515608.9|共9页
  • 作者

    Li Shijie; Lei Humin;

  • 作者单位

    Air Force Engn Univ, Air & Missile Def Coll, Xian 710051, Shaanxi, Peoples R China;

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