论文标题
粒子过滤器中的自适应在线差异估计：ALVAR估计器
Adaptive online variance estimation in particle filters: the ALVar estimator
论文作者
论文摘要
我们提出了一种新的方法 - 顺序蒙特卡洛方法或粒子过滤器中渐近方差的ALVAR估计器至估计。该方法可以自适应地调整[Olsson，J。和Douc，R。（2019）中提出的估计器的滞后。粒子过滤器中差异的数值稳定在线估计。 Bernoulli，25（2），pp。1504-1535]适用于非常通用的分布流和粒子过滤器，包括具有自适应重新采样的辅助粒子过滤器。该算法完全在线运行，从某种意义上说，它能够实时监视粒子滤波器的差异，并且平均而言，每次迭代中，恒定的计算复杂性和内存需求。至关重要的是，它不需要校准任何算法参数。仅根据传播粒子云的家谱估算差异，而没有其他模拟，例程仅需要对基础粒子算法的较小代码添加。最后，我们证明ALVAR估计器对于真正的渐近方差是一致的，因为颗粒的数量倾向于无穷大，并以数值为例，其优越性与现有方法的优越性。
We present a new approach-the ALVar estimator-to estimation of asymptotic variance in sequential Monte Carlo methods, or, particle filters. The method, which adjusts adaptively the lag of the estimator proposed in [Olsson, J. and Douc, R. (2019). Numerically stable online estimation of variance in particle filters. Bernoulli, 25(2), pp. 1504-1535] applies to very general distribution flows and particle filters, including auxiliary particle filters with adaptive resampling. The algorithm operates entirely online, in the sense that it is able to monitor the variance of the particle filter in real time and with, on the average, constant computational complexity and memory requirements per iteration. Crucially, it does not require the calibration of any algorithmic parameter. Estimating the variance only on the basis of the genealogy of the propagated particle cloud, without additional simulations, the routine requires only minor code additions to the underlying particle algorithm. Finally, we prove that the ALVar estimator is consistent for the true asymptotic variance as the number of particles tends to infinity and illustrate numerically its superiority to existing approaches.
