摘要
为解决多传感器组网系统的系统误差估计问题,基于多传感器多目标上报信息,研究并提出了一种多传感器多目标系统误差融合估计算法.算法构建了两级融合结构,即第一级对多传感器组合状态估计信息进行反馈融合以改善局部组合状态估计精度,从而间接改善系统误差的估计精度,而第二级对多目标系统误差估计信息进行融合以进一步提高系统误差的估计精度.蒙特卡洛仿真显示算法能有效融合利用多传感器多目标信息,实现多传感器系统误差的实时精确估计.
To solve the problem of sensor systematic bias estimation in sensor network, a systematic bias fusion estimation algorithm was presented based on multi-sensor multi-target information. To solve the fore- named problem, the algorithm was constituted a two layer fusion structure. In order to improve the precision of corresponding combination estimation, the first layer fuses the multi-sensor combination state estimation infor- mation with feedback, so the estimation precision of systematic bias can also be improved. While, the second layer fuses the multi-target systematic bias estimation information in order to further improve the estimation pre- cision. The monte-carlo simulation result shows that the algorithm can make sufficient use of the multi-sensor multi-target information with fusion structure, and achieve an exact and real-time estimation of the multi-sensor systematic bias.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2012年第6期835-841,共7页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金资助项目(60801049)
全国优秀博士学位论文作者专项资金资助项目(200443)
关键词
系统误差
传感器网络
误差配准
信息融合
systematic bias
sensor network
bias registration
information fusion
作者简介
宋强（1983-），男，江西新建人，工程师，songqiang8@sina．com．
