1) centralized measurement fusion
集中式观测融合
1.
The first is the centralized measurement fusion method,which combines the multi-sensor data by increasing the dimension of the measurement vector,whereas the second is the distributed measurement fusion method which combines the multi-sensor data by the weighting based on a linear minimum variance criterion,but the dimension of the measurement vector is not changed.
对于基于K alm an滤波的多传感器数据融合,有两种最优观测融合方法:第一种是集中式观测融合方法,它是通过增加观测向量的维数合并多传感器数据,而第二种是分布式观测融合方法,它是在线性最小方差准则下,通过加权合并多传感器数据,但观测向量维数不变。
2) centralized fusion
集中式融合
1.
For processing out-of-sequence measurements in the fusion center of a centralized fusion system which arise because of different time delays in transmitting measurements from various sensors to the center,an out-of-sequence measurement(ATFOOSM) algorithm for one-step-lag and multiple-step-lag out-of-sequence measurements is given.
针对集中式融合系统中从传感器到融合中心的传输延迟时间存在差异而导致融合中心出现乱序数据的问题,提出了一种能够处理单步延迟和多步延迟的乱序数据处理(ATFOOSM)算法。
3) Distributed measurement fusion
分布式观测融合
4) Multi-sensor central fusion tracking
集中式融合跟踪
5) centralized data fusion
集中式数据融合
6) centralized fusion system
集中式融合系统
1.
Aiming at complicated computing and difficulty of being realized when extending single sensor PHD filtering to the multi-sensor case by means of probability generating function,a multi-sensor multi-target sequential particle-PHD tracking algorithm is proposed based on the thought of sequential filtering for a centralized fusion system.
当采用概率母函数将单传感器PHD滤波推广到多传感器情形时,针对计算繁琐,难于实现的问题,本文基于集中式融合系统的有序滤波思想,提出多传感器、多目标有序粒子PHD跟踪算法,该算法通过选取与各传感器相关的重要性密度函数,层层更新各传感器的采样粒子,达到多传感器多目标有序PHD跟踪。
补充资料：大坝内部变形观测(见水工建筑物变形观测)
大坝内部变形观测(见水工建筑物变形观测)
daba neibubianxing guanCe大坝内部变形观测见水工建筑物变形观测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条