Sensor Registration in Asynchronous Data Fusion  

异步数据融合的传感器配准(英文)

作　　者：胡士强[1] 张天桥[1] 

机构地区：[1]北京理工大学机电工程学院,北京100081

出　　处：《Journal of Beijing Institute of Technology》2001年第3期285-290,共6页北京理工大学学报（英文版）

基　　金：MinisterialLevelFoundation

摘　　要：To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bias and sensor frame tilt errors in multisensor systems with asynchronous data. Simulation results is presented to demonstrate the performance of these approaches. The least squares approach can compress measurements to any time. The Kalman filter algorithm can detect registration errors and use the information to converge tracks from independent sensors. This is particularly important if the data from the sensors are to be fused.针对异步多传感器系统 ,研究一种能有效估计配准误差并消除其影响的算法 .提出用最小二乘法和卡尔曼滤波算法分别进行时间配准和估计失调误差和姿态误差 .仿真结果证实了算法的有效性 .采用最小二乘法可以将数据压缩“对齐”到任意时刻 ,采用卡尔曼滤波算法可以估计并消除配准误差的影响 ,这对多传感器的数据融合具有重要的意义 .

关 键 词：data fusion multisensor system REGISTRATION Kalman filter 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]