论文标题
卡尔曼滤波器估计的下限不确定性中的特征值分析
Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates
论文作者
论文摘要
在本文中，我们关注的是Kalman过滤中的误差降低降低问题：传感器对数据融合/估计中心释放一组测量，该中心对动态模型具有完美的了解，以允许其估算状态，同时防止其估算给定准确性的州。我们提出了一个测量噪声操纵方案，以确保降低状态的估计准确性。我们提出的方法使用特征值分析中的数学工具确保了卡尔曼过滤器的稳态估计误差的较低限制。
In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy. We propose a measurement noise manipulation scheme to ensure lower-bound on the estimation accuracy of states. Our proposed method ensures lower-bound on the steady state estimation error of Kalman filter, using mathematical tools from eigen value analysis.
