1) two-stage r
带观测噪声系统
1.
They include univariable and multivariable two-stage recursive least squares-recursive extended least squares (RLS-RELS) and two-stage recursive least squares-pseudo-inverse (RLS-PI) algorithms.
并将这些算法推广到带观测噪声系统参数估计的问题，给出了带观测噪声系统参数估计的一些新方法和新算法，其中包括两段RELS-Gevers-Wouters算法和三段RLS-PI-Gevers-Wouters算法，解决了用普通最小二乘法估计带观测噪声系统未知参数的有偏问题。
2) systems with multiplicative noise
带乘性噪声系统
1.
On the basis of wavelet transformation and multiscale analysis, the paper combines the model-based dynamic system analysis method with the multiscale information transformation method based on the statistical characteristics and proposes multiscale optimal filtering fusion algorithm for systems with multiplicative noise.
利用小波变换和多尺度分析的思想,将基于模型的动态系统分析和基于统计特性的多尺度信号变换方法相结合,提出了在线性最小方差意义下的带乘性噪声系统的多尺度最优滤波融合算法。
2.
A partitioned optimal filtering algorithm for multi-channel systems with multiplicative noise among observation channels is proposed.
针对多通道观测环境下带乘性噪声系统的最优滤波问题,提出了1种状态最优滤波的分部算法。
3.
To the fusion problem of backward filtering and deconvolution of stochastic systems with multiplicative noise and observed by multisensor, an optimal fusion algorithm is provided.
针对多传感器观测环境下带乘性噪声系统的逆向最优滤波与反褶积融合估计问题 ,本文提出了 1种基于极大似然准则的最优融合算法。
3) system noise bandwidth
系统噪声带宽
4) measurement noise
观测噪声
1.
The fusion algorithm of multi-sensor measurement noise update estimate on out-of-sequence;
无序量测下的多传感器观测噪声融合估计
2.
It is well known that the successful applications of the Kalman filter are dependent on whether the prior knowledge of the statistical characteristics of the measurement noise is known.
众所周知,卡尔曼滤波的成功应用需要事先准确知道观测噪声的统计特性。
5) observation noise
观测噪声
1.
The ionospheric delay can be weakened by multi-frequency observations,but pseudorange errors such as multipath errors and observation noises are magnified to different degrees due to using multi-frequency methods.
多频测距系统可以借助多频观测数据削弱电离层延迟的影响,但多频改正算法在改正电离层延迟项的同时会不同程度地放大多路径误差、观测噪声等伪距误差的影响。
2.
The local geoid or gravity anomaly as an example is refined by the fusion of the simulated geoid height and gravity anomaly data,and the effects of observation noise level and non-stationary noise to the data fusion results are analyzed.
以融合大地水准面高和重力异常数据精化局部大地水准面或重力异常为例,利用模拟数据分析了不同大小的观测噪声和非稳态误差对数据融合结果的影响。
6) observational noises
观测噪声
1.
First analyzes the influence of observational noises about temporal correlation for Kalman filter,and gives a recursive formula of Kalman filtering according to linear unbiased minimum variance estimator criterion and a solution of data storage at the same time.
针对一般时间相关观测噪声进行研究,分析它们对Kalman结果的影响,然后根据状态估计为线性无偏最小方差估计的准则,给出测量噪声时间相关时的Kalman递推公式,同时也考虑相关数据的存储问题,最后通过数字模拟验证算法的有效性。
2.
This paper analyses the influence of observational noises about temporal correlation for Kalman Filter firstly.
本文针对一般时间相关观测噪声进行研究,分析了它们对卡尔曼滤波结果的影响,然后根据状态估计为线性无偏最小方差估计的准则,给出了测量噪声时间相关时的卡尔曼滤波递推公式,同时也考虑了相关数据的存储问题,最后通过实例计算验证了算法的有效性。
3.
The research analysed the influence of observational noises about temporal correlation,and gives recursive formula of Kalman Filtering.
在动态定位数据处理中,动态定位的精度和可靠性除受观测偶然误差和系统误差的影响外,还受时间相关的观测噪声的影响。
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