论文标题
高斯粒子滤波器
Multiplicative Gaussian Particle Filter
论文作者
论文摘要
我们提出了一种基于抽样的新方法，以近似推断过滤问题。我们的方法没有像在粒子过滤器中完成的有限状态近似条件分布，而是通过一组连续函数的加权函数近似分布。该方法的核心是使用采样来近似贝叶斯过滤器中的乘法。我们提供理论分析，提供采样条件以提供良好的近似值。接下来，我们专门研究高斯人加权总和，并显示高斯人的性质如何实现封闭形式的过渡和有效的乘法。最后，我们在机器人定位问题上进行了初步实验，并将性能与粒子过滤器进行比较，以证明所提出的方法的潜力。
We propose a new sampling-based approach for approximate inference in filtering problems. Instead of approximating conditional distributions with a finite set of states, as done in particle filters, our approach approximates the distribution with a weighted sum of functions from a set of continuous functions. Central to the approach is the use of sampling to approximate multiplications in the Bayes filter. We provide theoretical analysis, giving conditions for sampling to give good approximation. We next specialize to the case of weighted sums of Gaussians, and show how properties of Gaussians enable closed-form transition and efficient multiplication. Lastly, we conduct preliminary experiments on a robot localization problem and compare performance with the particle filter, to demonstrate the potential of the proposed method.
