用于标记随机有限集跟踪的多传感器联合自适应出生采样器
Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking
摘要：
本文提出了一种可扩展的，多传感器测量自适应航迹起始技术标记随机有限集滤波器。多传感器测量自适应出生集分布的朴素构造导致传感器数量中的新生组件的指数数量。建立了标记多重Bernoulli随机有限集出生密度的截断准则。所提出的截断准则被证明在广义标记多伯努利后验密度中具有有界L1误差。这个标准是用来构建一个吉布斯采样器，产生一个截断的测量生成的标记的多伯努利出生分布与二次复杂的传感器的数量。假设线性高斯似然的条件采样分布的封闭形式的解决方案，旁边的近似解决方案，使用蒙特卡洛重要性抽样。多个仿真结果验证了截断准则的有效性，以及复杂度的降低。
英文摘要：
This paper provides a scalable, multi-sensor measurement adaptive track initiation technique for labeled random finite set filters. A naive construction of the multi-sensor measurement adaptive birth set distribution leads to an exponential number of newborn components in the number of sensors. A truncation criterion is established for a labeled multi-Bernoulli random finite set birth density. The proposed truncation criterion is shown to have a bounded L1 error in the generalized labeled multi-Bernoulli posterior density. This criterion is used to construct a Gibbs sampler that produces a truncated measurement-generated labeled multi-Bernoulli birth distribution with quadratic complexity in the number of sensors. A closed-form solution of the conditional sampling distribution assuming linear Gaussian likelihoods is provided, alongside an approximate solution using Monte Carlo importance sampling. Multiple simulation results are provided to verify the efficacy of the truncation criterion, as well as the reduction in complexity.
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