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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >A fast-weighted Bayesian bootstrap filter for nonlinear model state estimation
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A fast-weighted Bayesian bootstrap filter for nonlinear model state estimation

机译：用于非线性模型状态估计的快速加权贝叶斯自举滤波器

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摘要

In discrete-time system analysis, nonlinear recursive state estimation is often addressed by a Bayesian approach using a resampling technique called the weighted bootstrap. Bayesian bootstrap filtering is a very powerful technique since it is not restricted by model assumptions of linearity and/or Gaussian noise. The standard implementation of the bootstrap filter, however, is not time efficient for large sample sizes, which often precludes its utilization. We propose an approach that dramatically decreases the computation time of the standard bootstrap filter and at the same time preserves its excellent performance. The time decrease is realized by resampling the prior into the posterior distribution at time instant k by using sampling blocks of varying size, rather than a sample at a time as in the standard approach. The size of each block resampled into the posterior in the algorithm proposed here depends on the product of the normalized weight determined by the likelihood function for each prior sample and the sample size N under consideration.
机译：在离散时间系统分析中，非线性递归状态估计通常通过贝叶斯方法使用称为加权自举的重采样技术解决。贝叶斯自举滤波是一种非常强大的技术，因为它不受线性和/或高斯噪声的模型假设的限制。但是，自举过滤器的标准实现方式对于大样本量而言并不节省时间，这通常会妨碍其使用。我们提出了一种方法，该方法可以大大减少标准自举滤波器的计算时间，同时保留其出色的性能。通过使用大小变化的采样块（而不是像标准方法那样一次采样），在时刻k将先验先验重新采样到后验分布中，从而实现了时间的减少。在此处提出的算法中，重新采样到后验中的每个块的大小取决于每个先前样本的似然函数所确定的归一化权重与所考虑的样本大小N的乘积。

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