论文标题
贝叶斯融合数据分配的粒子估计值
Bayesian Fusion of Data Partitioned Particle Estimates
论文作者
论文摘要
我们提出了一种贝叶斯数据融合方法，以近似于仅访问数据子集的粒子估计集合的后验分布。我们的方法依赖于通过蒙特卡洛方法对模型参数的近似概率推断，然后进行了与多重重要性抽样相关的更新和重新样本方案，以结合最初估计的信息。我们显示该方法是在粒子极限中收敛的，直接适用于在多传感器数据融合问题上应用，通过证明对多传感器开普勒轨道确定问题和仅轴承跟踪问题的功效。
We present a Bayesian data fusion method to approximate a posterior distribution from an ensemble of particle estimates that only have access to subsets of the data. Our approach relies on approximate probabilistic inference of model parameters through Monte Carlo methods, followed by an update and resample scheme related to multiple importance sampling to combine information from the initial estimates. We show the method is convergent in the particle limit and directly suited to application on multi-sensor data fusion problems by demonstrating efficacy on a multi-sensor Keplerian orbit determination problem and a bearings-only tracking problem.
