随机粒子流滤波器的稳定性和收敛性
Stability and Convergence of Stochastic Particle Flow Filters
摘要：
在本文中，我们研究动态特性的粒子流最近派生的参数化家庭的随机粒子流过滤器的非线性过滤和贝叶斯推理。特别是，我们建立了粒子保持所需的后验分布，没有高斯假设的测量可能性。采用李雅普诺夫稳定性的概念，我们进一步表明，粒子保持接近，但不收敛到后验分布的最大似然估计。结果表明，该随机粒子流滤波器族保持了粒子流的稳定性。
英文摘要：
In this paper, we examine dynamic properties of particle flows for a recently derived parameterized family of stochastic particle flow filters for nonlinear filtering and Bayesian inference. In particular, we establish that particles maintain desired posterior distribution without the Gaussian assumption on measurement likelihood. Adopting the concept of Lyapunov stability, we further show that particles stay close but do not converge to the maximum likelihood estimate of the posterior distribution. The results demonstrate that stability of particle flows is maintained for this family of stochastic particle flow filters.
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