On-line Support Vector Regression of the transition model for the Kalman filter
On-line Support Vector Regression of the transition model for the Kalman filter
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DOI：
10.1016/j.imavis.2012.09.008
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发表时间：
2013-06-01
影响因子：
4.7
通讯作者：
Di Stefano, Luigi
中科院分区：
文献类型：
--
作者：
Salti, Samuele;Di Stefano, Luigi
递归贝叶斯估计(RBE)是视觉跟踪的一种广泛的解决方案，也适用于从噪声测量递归估计隐藏状态的其他领域。从实践的角度来看，RBE过滤器的部署受到关于过程和测量统计的完整知识的假设的限制。这些丢失的信息令牌导致过滤器参数的近似或甚至是不知情的分配。遗憾的是，使用错误的过渡或测量模型可能会导致很大的估计误差或发散，即使部署了原本最优的滤波器也是如此。本文提出了一种基于支持向量机回归的过渡模型在线学习方法。然后介绍了这种用于线性/高斯滤波器的通用框架的特化，我们称之为支持向量卡尔曼(SVK)，其性能优于标准的非自适应卡尔曼滤波器以及用于处理未知过渡模型的广泛解决方案，例如交互多模型(IMM)滤波器。(C)2012爱思唯尔B.V.保留所有权利。
Recursive Bayesian Estimation (RBE) is a widespread solution for visual tracking as well as for applications in other domains where a hidden state is estimated recursively from noisy measurements. From a practical point of view, deployment of RBE filters is limited by the assumption of complete knowledge on the process and measurement statistics. These missing tokens of information lead to an approximate or even uninformed assignment of filter parameters. Unfortunately, the use of the wrong transition or measurement model may lead to large estimation errors or to divergence, even when the otherwise optimal filter is deployed. In this paper on-line learning of the transition model via Support Vector Regression is proposed. The specialization of this general framework for linear/Gaussian filters, which we dub Support Vector Kalman (SVK), is then introduced and shown to outperform a standard, non adaptive Kalman filter as well as a widespread solution to cope with unknown transition models such as the Interacting Multiple Models (IMM) filter. (C) 2012 Elsevier B.V. All rights reserved.