MODELS AND ALGORITHMS FOR TRACKING USING VARIABLE DIMENSION PARTICLE FILTERS
MODELS AND ALGORITHMS FOR TRACKING USING VARIABLE DIMENSION PARTICLE FILTERS
复制标题
使用可变尺寸粒子过滤器进行跟踪的模型和算法
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
J. Vermaak
中科院分区：
文献类型：
--
作者：
S. Godsill;J. Vermaak
在本文中，我们将讨论修改跟踪模型，并从连续和批量数据估计的顺序蒙特卡罗算法。提出了一种新的跟踪模型，它涉及到一个动态模型的隐藏状态值和它的到达时间。通过这种方式，我们的目标是有一个更灵活和简约的时变状态特征，这是更适合使用贝叶斯滤波估计表示。为了在这种情况下进行推理，提出了新的粒子滤波器和平滑器的情况下，状态过程到达未知的时间，通常是不同的观察到达时间。
In this paper we discuss modifications to tracking models, and sequential Monte Carlo algorithms for their estimation from sequential and batch data. New models for tracking are proposed which involve a dynamical model on both the hidden state value and its arrival times. In this way we aim to have a more flexible and parsimonious representation of time-varying state characteristics which is more amenable to estimation using Bayesian filtering. In order to perform inference in this scenario new particle filters and smoothers are proposed for cases where the state process arrives at unknown times that are generally different from the observation arrival times.