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| An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering |
| Tong-yang Jiang, Mei-qin Liu, Xie Wang, Sen-lin Zhang |
| State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli (SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets. Since most clutter measurements do not participate in the update step, the computing time is reduced significantly. Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.
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Received: 25 January 2014
Published: 06 June 2014
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一种用于多目标滤波的有效量测驱动序列蒙塔卡洛多伯努利滤波器
研究目的：序列蒙塔卡洛多伯努利滤波器的计算复杂度随量测个数线性增长，尤其在杂波环境下，量测中包含大量杂波量测，如果考虑所有的量测，将大大增加计算量，并且杂波量测也会降低滤波精度。因此，有必要从初始量测中区分可能的生存目标量测、新生目标量测和杂波量测，从而消除杂波量测，提高多目标滤波的实时性。
创新要点：利用跟踪门技术区分可能的生存目标量测、新生目标量测和杂波量测，之后用生存目标量测更新生存和新生目标，而新生目标量测只用来更新新生目标，从而在保证多目标滤波精度前提下，提高了多目标滤波的实时性。
方法提亮：首次利用跟踪门技术来区分可能的生存目标量测、新生目标量测和杂波量测，并提出了量测驱动方法用于序列蒙塔卡洛多伯努利滤波器。
重要结论：同初始的序列蒙塔卡洛多伯努利滤波器相比，本文所提方法在保证多目标滤波精度前提下，提高了多目标滤波的实时性。
关键词：
量测驱动,
序列蒙塔卡洛,
多伯努利滤波,
跟踪门技术,
多目标滤波
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