摘要
针对如何提高伺服系统多传感器测量数据的可信度,提出了一种状态最优估计融合算法。该算法依据Bayes后验估计理论,把求n个测量数据的状态最优估计转化为求出满足Y(需要测量的状态值)的最大后验概率maxP(Y|E)的估计值Y(E?)的问题,求得多传感器对同一目标的状态测量值的最优值,来提提高伺服测量数据的可信度。该算法应用到具有三种传感器的伺服系统中,实验结果证明,系统能持久稳定、高精度地跟踪运动目标,大大提高了系统的稳定性。
A state optimization algorithm is suggested in view of how to improve the reliability of multi-sensor data in a servo system. According to Bayes posterior estimation theory, the algorithm requires to convert the state optimization estimation of n data into seeking the estimated value Y(E) of the largest posterior probability maxP less than or equal Y-E>of Y, thus obtaining the optimization state value of multiple sensors to the same target and improving the reliability of theodolite. The algorithm has been applied to a servo system with three kinds of sensors and moving targets can be stably tracked with high-accuracy for a long time and system's stability has been greatly improved.
出处
《光电工程》
EI
CAS
CSCD
北大核心
2005年第6期9-11,30,共4页
Opto-Electronic Engineering
关键词
数据融合
伺服控制系统
复合控制
目标跟踪
Data fusion
Servo control system
Composite control
Target tracking
