GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
复制标题
DOI：
10.1007/s10514-009-9119-x
复制
发表时间：
2009-07-01
影响因子：
3.5
通讯作者：
Fox, Dieter
中科院分区：
文献类型：
--
作者：
Ko, Jonathan;Fox, Dieter
贝叶斯滤波是递归估计动态系统状态的一般框架。每个贝叶斯过滤器的关键组成部分是概率预测和观测模型。本文展示了非参数高斯过程（GP）回归如何用于从训练数据中学习此类模型。我们还展示了如何高斯过程模型可以集成到不同版本的贝叶斯滤波器，即粒子滤波器和扩展和无迹卡尔曼滤波器。由此产生的GP-Bayes滤波器可以有几个优点，标准（参数）过滤器。最重要的是，GP-Bayes滤波器不需要系统的精确参数模型。如果有足够的训练数据，与参数模型相比，它们可以提高跟踪精度，并且随着模型不确定性的增加，它们会优雅地降级。这些优点源于GPS同时考虑系统中的噪声和模型中的不确定性。如果近似参数模型可用，则可以将其合并到GP中，从而进一步提高性能。在实验中，我们展示了不同的属性GP-Bayes过滤器使用的数据收集与自主微型飞艇以及合成数据。
Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Key components of each Bayes filter are probabilistic prediction and observation models. This paper shows how non-parametric Gaussian process (GP) regression can be used for learning such models from training data. We also show how Gaussian process models can be integrated into different versions of Bayes filters, namely particle filters and extended and unscented Kalman filters. The resulting GP-BayesFilters can have several advantages over standard (parametric) filters. Most importantly, GP-BayesFilters do not require an accurate, parametric model of the system. Given enough training data, they enable improved tracking accuracy compared to parametric models, and they degrade gracefully with increased model uncertainty. These advantages stem from the fact that GPs consider both the noise in the system and the uncertainty in the model. If an approximate parametric model is available, it can be incorporated into the GP, resulting in further performance improvements. In experiments, we show different properties of GP-BayesFilters using data collected with an autonomous micro-blimp as well as synthetic data.