摘要
在不完全量测下估计系统状态时,状态的稳态误差协方差与各个传感器精度指标有关.今提出一种新算法.可以根据估计误差协方差确定出传感器精度的上-下确界.算法根据稳态卡尔曼滤波的估计误差协方差表达式,推出传感器探测概率以及量测噪声方差指标的容差,并结合线性矩阵不等式求出传感器量测噪声方差的上-下界.根据这些结果,可以对给定的估计误差协方差,采用传感器精度指标的下界,从而在满足其他工程要求的前提下,放宽采样频率,降低传感器成本.
In estimating system states in intermittent observations,the steady-state error covariance depends on the precision of sensors.We propose a novel algorithm for determining the required infimum-supremum of sensor accuracy based on the given estimation error covariance in intermittent observations.Starting from the estimation error covariance expressions of the steady-state Kalman filter;this algorithm determines the sensor detection probability and the admissible upper bound of the measurement noise covariance by using the linear matrix inequality.According to these results,the infimum requirement for sensor accuracies may be adopted under the given the estimation error covariance,thus relaxing the requirements of sampling frequency and reducing the costs of measuring equipment as long as other engineering requirements are satisfied.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2013年第4期508-512,共5页
Control Theory & Applications
基金
国家自然科学基金资助项目(60972119,61170243)
河南省科技人才创新项目资助项目(114100510001)
河南省基础与前沿技术研究计划资助项目(132300410148)
河南省教育厅科学技术研究重点资助项目(13A413066)
关键词
状态估计
传感器精度
不完全量测
方差约束
state estimation
sensor accuracy
intermittent observations
covariance constraint
