摘要
在实际的工程项目中,采用多个传感器进行测量可以得到更好的状态估计值,多传感器测量已经得到广泛应用。但由于测量噪声等系统误差的存在,测量结果往往是不准确的,这就需要对测量数值进行数据融合和状态估计。为实现对多传感器系统的状态估计,采用集中式融合方法对信息进行融合,通过引入广义测量向量,设计卡尔曼滤波器,对系统进行状态估计。估计值准确率较高。最后结合无人驾驶飞机的实例,以无人驾驶飞机控制中心的数据融合为背景,同时基于多传感器实时数据融合系统,分别就3个和4个传感器的情况进行仿真实验,取得了良好的融合估计效果,并与其他方法进行对比,实验结果表明了方法的有效性,在工程上有一定的实用价值。
In engineering practice,estimations of state variables are deserved more close to their exact values with multi-sensor measurement, and multi-sensor measurement has been widely applied,but the measurement results are often inaccurate because the system noises like measurement noises.In order to realize state estimating for multi-sensor system,a centralized fusion algorithm is proposed to fuse information,and a Kalman filter with generalized measurement vector is designed to estimate system slate.High accuracy rate of estimation value is achieved.Compared with another method,the data fusion of the control center of pilotless aircraft is introduced as a background to make some simulations with three or four sensors respectively.The simulations results show that the proposed method is very efficient.
出处
《控制工程》
CSCD
北大核心
2010年第S2期101-103,167,共4页
Control Engineering of China
基金
上海市基础研究重点资助项目(09JC1408000)
关键词
多传感器
广义测量向量
无人机
状态估计
集中式
卡尔曼
multi-sensor
generalized measurement vector
pilotless aircraft
state estimation
centralize
Kalman
