1) Kalman filter
Kalman过滤器
1.
This paper uses a panel data approach that efficeiently combines the time series and crosssection information in the term structure data,and applies the Kalman filter to estimate a parametric verision of the model by psendo maxinum likelihood and extract the implied variable.
采用能有效地将期限结构数据中的时间序列和截面信息结合起来的成组数据方法 ,并借助于 Kalman过滤器通地假性最大可能性对模型的系数进行估值并提炼出隐含的状态变
2) Kalman filter
Kalman滤波器
1.
Application of Kalman filter to measurement of sound power for medical ultrasonic diagnostic equipment;
Kalman滤波器在医用超声诊断设备声功率计量中的应用
2.
Self-tuning decoupled fusion Kalman filter based on Riccati equation;
基于Riccati方程的自校正解耦融合Kalman滤波器
3.
Application of Kalman Filter in a Single Automatic Tracking of Moving Targets;
Kalman滤波器在单运动目标自动跟踪中的应用
3) Kalman filtering
Kalman滤波器
1.
Multiple model Kalman filtering for MEMS-IMU/GPS integrated navigation;
MEMS-IMU/GPS组合导航中的多模态Kalman滤波器设计
2.
In this paper,a Kalman-CS scene-based correction algorithm which combines the Kalman filtering algorithm with the constant-statistics (CS) algorithm is proposed.
本文将Kalman滤波器校正算法与常数统计(CS)校正算法相结合,提出了Kalman-CS校正算法。
3.
A new interval Kalman filtering (IKF) is described firstly, then uncertain dynamics modelling of an integrated GPS/INS system is discussed.
针对具有不确定动态模型参数的GPS/INS组合导航系统,首先介绍一种新型的区间Kalman滤波器,讨论了GPS/INS组合系统中模型参数不确定性的问题,分析了惯性传感器建模中相关时间常数的区间特性,并建立了适合非线性特性的GPS/INS组合系统的扩展区间卡尔曼滤波器。
4) Kalman filter
Kalman 滤波器
1.
Comparing with Kalman filter the strong tracking filter is well in adaptive maneuvering target and dependance on the choice of model.
在与传统的 Kalman 滤波器相比,强跟踪滤波器在机动目标的适应,运动模型的依赖性方面都有了很大的提高。
5) Kalman filtering correction
Kalman滤波器法
1.
In this paper, we introduce the approach of constant range correction, Kalman filtering correction and trace along trajectory correction.
本文介绍了恒定范围统计法、Kalman滤波器法和轨迹跟踪法。
6) Combined Kalman Filtering
组合Kalman滤波器
1.
The Application of Combined Kalman Filtering to the Kinematic GPS Positioning;
组合Kalman滤波器在GPS动态定位中的应用
补充资料：有过
有过 有过 即有病。《素问·脉要精微论》：“故乃可诊有过之脉。”《灵枢·经脉》：“督脉之别，名曰长强……实则脊强，虚则头重，高摇之挟脊之有过者，取之所别也。”
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