|
|
|
| A novel approach of noise statistics estimate using H∞ filter in target tracking |
| Xie Wang, Mei-qin Liu, Zhen Fan, Sen-lin Zhang |
| State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
|
|
|
Abstract Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox. First, we apply the H∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost, which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.
|
|
Received: 17 August 2015
Published: 04 May 2016
|
|
|
目标跟踪中一种新的基于H∞滤波器的噪声统计特征估计方法
目的：在目标跟踪滤波估计算法设计中，噪声的统计特征必不可少。现有的大多数对噪声统计特征的线性估计算法都是基于卡尔曼滤波器或贝叶斯估计器，至少需要噪声统计特征的先验知识。在此前提下，本文提出的估计算法不需要噪声统计特征的先验知识。引入H∞滤波器，获得系统状态估计，并可以获得更精确的残差信息对噪声进行估计。
创新点：假设噪声为高斯分布，但不需要噪声统计特征的先验知识；在算法设计中引入H∞滤波器，获得更准确的残差信息。
方法：假设噪声统计特征的先验知识未知，通过H∞滤波器获得系统状态估计；通过得到的系统状态估计值和量测值，可以得到残差样本序列；结合数理统计知识，通过得到的残差样本序列对过程噪声和量测噪声的均值、协方差进行估计。
结论：与基于卡尔曼滤波器的同一框架下得到的估计方法相比，本文中的算法可以得到更精确的估计结果（图2、4-6）。
关键词：
噪声估计,
H∞滤波,
目标跟踪
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
