摘要
针对多传感器的系统误差配准问题,研究了系统误差时变的情况,提出了一种基于迭代扩展卡尔曼滤波(IEKF)的配准算法。该算法将目标的运动状态和传感器系统误差组合在同一状态方程中,构建扩维状态的系统动态方程,采用IEKF的方法对目标状态和系统误差进行联合估计。仿真结果表明,与采用EKF和UKF的方法相比,该算法能取得和UKF相近估计精度,并且时间效率和EKF相当。
For the systematic registration errors of multi-sensor,a registration model for space biases is given and a registration algorithm based on iterated extend Kalman filter（IEKF）is presented.By incorporating target states and systematic registration errors into one state equation,the dynamic equation of the augmented state equation is constructed.Then a registration algorithm based on IEKF is proposed to estimate target states and systematic registration errors simultaneously.To compare it with EKF and UKF,the result shows the proposed method has the same or even better accuracy to UKF and the similarity computation with EKF.
出处
《传感技术学报》
CAS
CSCD
北大核心
2010年第5期713-716,共4页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金重点资助项目资助(60634030)
航空科学基金项目资助(2007ZC53037)
国家自然科学基金青年科学基金项目资助(60702066)
航空基金项目资助(20090853013)
西北工业大学翱翔之星计划资助
关键词
数据融合
多传感器网络
误差配准
迭代EKF
data fusion
multi-sensor network
space registration
iterated extend Kalman filter
