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自校正kalman滤波器
相关语句
  self-tuning kalman filter
     A Self-Tuning Kalman Filter with Input Estimation and Its Application
     带输入估计的自校正Kalman滤波器及其应用
短句来源
     Compared with the Riccati equation-based information fusion Kalman filter weighted by matrices, it can obviously reduce the computational burden, and is suitable for real time applications, It can be applied to design information fusion self-tuning Kalman filter for systems with unknown noise statistics.
     与基于Riccati方程按矩阵加权的信息融合Kalman滤波器相比,可明显减小计算负担,便于实时应用,可用于设计含未知噪声统计系统的信息融合自校正Kalman滤波器.
短句来源
     A SELF-TUNING KALMAN FILTER WITH THE COLOURED OBSERVATION NOISE AND INPUT ESTIMATION AND ITS APPLICATION
     带有色观测噪声和输入估计的自校正Kalman滤波器及其应用
短句来源
     For discrete-time systems with unknown constant input and unknown noise statistics, using the modem time series analysis method, based on ARMAX innovation model, this paper presents a new self-tuning Kalman filter with input estimation.
     对于带未知常的输入和带未知噪声统计的离散时间定常系统,本文用现代时间序列分析方法,基于ARMAX新息模型,提出了一种新的带输入估计的自校正Kalman滤波器
短句来源
     A self-tuning Kalman filter in observable and noncontrolable stochastic system
     可观非可控随机系统的自校正Kalman滤波器
短句来源
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  “自校正kalman滤波器”译为未确定词的双语例句
     From the point of view of time series analysis,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering tech-nique.
     本文从时间序列分析观点,基于观测过程的 CARMA 新息模型,提出了稳态 Kalman 滤波增益估计的两种新算法及相应的自校正 Kalman 滤波器,形成一种新的自适应 Kalman 滤波技术.
短句来源
     By using the modern time series analysis method and based on the ARMA innovation model,two new algorithms of steady state Kalman filter gain are presented,and their equivalence is proved. The self tuning Kalman filters can be implemented by using a recursive identifier of parameters for the ARMA innovation model,in conjunction with the new algorithms. A simulation example shows usefulness of the proposed algorithms.
     应用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增益的两种简单的新算法,并证明了它们的等价性.应用ARMA新息模型参数的递推辨识器伴随新算法,可实现自校正Kalman滤波器.仿真例子说明了其有效性.
短句来源
  相似匹配句对
     Self-tuning Multisensor Measurement Fusion Kalman Filter
     自校正多传感器观测融合Kalman滤波器
     Self-tuning Weighted Measurement Fusion Kalman Filter
     自校正加权观测融合Kalman滤波器
     Two-sensor Self-tuning Information Fusion Kalman Filter
     两传感器自校正信息融合Kalman滤波器
短句来源
     Self- tuning Information Fusion Kalman Filter Weighted by Scalars
     自校正标量加权信息融合Kalman滤波器
短句来源
     A self-tuning Kalman filter in observable and noncontrolable stochastic system
     可观非可控随机系统的自校正Kalman滤波器
短句来源
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From the point of view of time series analysis,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering tech-nique.New algorithms are simpler than that of Mehra and Tajima.As an application example,self-tuning α-β tracking filter with input estimation is given,and simulation results show the effectivenessof the new algorithms.

本文从时间序列分析观点,基于观测过程的 CARMA 新息模型,提出了稳态 Kalman 滤波增益估计的两种新算法及相应的自校正 Kalman 滤波器,形成一种新的自适应 Kalman 滤波技术.新算法比Mehra 和 Tajima 的算法简单.作为应用例子,对于一个简单的跟踪系统,导出了带输入估计的自校正α-β滤波器,仿真结果说明了新算法的有效性.

For discrete-time systems with unknown constant input and unknown noise statistics, using the modem time series analysis method, based on ARMAX innovation model, this paper presents a new self-tuning Kalman filter with input estimation. As an application example, a new self-tuning α-β-γ tracking filter with input estimation is presented, and simulation results show its usefulness.

对于带未知常的输入和带未知噪声统计的离散时间定常系统,本文用现代时间序列分析方法,基于ARMAX新息模型,提出了一种新的带输入估计的自校正Kalman滤波器。作为一个应用例子,提出了带输入估计的自校正α-β-γ跟踪滤波器,仿真结果说明了其有效性。

By using the modern time series analysis method and based on the ARMA innovation model,two new algorithms of steady state Kalman filter gain are presented,and their equivalence is proved.The self tuning Kalman filters can be implemented by using a recursive identifier of parameters for the ARMA innovation model,in conjunction with the new algorithms.A simulation example shows usefulness of the proposed algorithms.

应用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增益的两种简单的新算法,并证明了它们的等价性.应用ARMA新息模型参数的递推辨识器伴随新算法,可实现自校正Kalman滤波器.仿真例子说明了其有效性.

 
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