Robust Kalman Filtering based on Multiple Hypothesis Techniques
Robust Kalman Filtering based on Multiple Hypothesis Techniques
复制标题
基于多重假设技术的鲁棒卡尔曼滤波
DOI：
10.1109/sice.2006.315271
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
W. Ra
中科院分区：
文献类型：
--
作者：
I. Whang;W. Ra
针对具有参数不确定性的线性系统，提出了一种新的鲁棒状态估计器。将影响系统的不确定性视为量化参数不确定性的未知序列。然后利用多假设检验技术对不确定参数序列的假设进行处理，得到精确的鲁棒估计量。然而，由于精确滤波器必须处理不断扩展的假设，因此提出了一种基于零扫描返回概念的次优估计器。通过鲁棒卡尔曼滤波的一个基准算例，比较了所提出的鲁棒卡尔曼滤波与现有鲁棒卡尔曼滤波的性能
In this paper, a new robust state estimator for linear systems with parametric uncertainties is proposed. The uncertainties affecting the system are regarded as unknown sequences of quantized parametric uncertainties. And then the exact robust estimator is derived by handling the uncertainty parameter sequence hypotheses by means of multiple hypotheses testing (MHT) techniques. However, since the exact filter has to treat ever expanding hypotheses, a suboptimal estimator based on zero scan back concept is proposed. A benchmark example for robust Kalman filtering is demonstrated to compare the performance of the proposed filter with those of an existing robust Kalman filter