In order to take advantage of these two properties, we propose a new combined filter. This filter uses the prediction principle of the Kalman filter. Then, through an adaptation step, it uses the principle of the information filter for the update step. A detailed study of the calculation ...
Kalman filtering-based information fusion Wiener filter of autoregressive moving average signals基于Kalman滤波的自回归滑动平均信号信息融合Wiener滤波器 DENG Z L,GAO Y.Kalman Filtering-based Information Fusion Wiener Filter of Autoregressive Moving Average Signals.Control Theory and Applications. 2005... DENG...
The unscented filter u... Y Yang,J Li - International Conference on Measurement 被引量: 4发表: 2012年 Comparison of a grid-based filter to a Kalman filter for the state estimation of a maneuvering target Providing accurate state estimates of a maneuvering target is an important problem. This...
Kalman Filter Information The Kalman Filter was invented to solve a problem in spacecraft navigation, but the technique is relevant not only to navigation but also to other problems where incomplete or inconsistant observations must be combined with a (possibly incomplete) state of a system. This...
1.Its accuracy is higher than that of each localself-tuning Wiener filter,the algorithm is simple,and is suitable for real time applications.它的精度比每个局部自校正Wiener滤波器精度都高,且算法简单,便于实时应用。 4)Information fusion Kalman filter信息融合Kalman滤波器 ...
By using the Kalman filtering method and the linear minimum variance optimal fusion rule weighted by matrices,a multisensor information fusion Wiener filter is presented for the multichannel autoregressive moving average(ARMA) signals with white observat
The UDU factorization of the Kalman filter is known for its numerical stability; this paper extends the technique to the information filter. A distinct characteristic of the new algorithm is that measurements can be processed as vectors, while the classic UDU factorization requires scalar measurement ...
The application of enginerring has proved that the robustness of standard Kalman filter is not very good. When we meet the real problem, it will result to the divergence of the filter if the constructed model is unfit on the real process. Regarding this problem, we prefer multisenson informat...
Its accuracy is higher than that of each local self-tuning Wiener filter,the algorithm is simple,and is suitable for real time applications. 它的精度比每个局部自校正Wiener滤波器精度都高,且算法简单,便于实时应用。4) Information fusion Kalman filter 信息融合Kalman滤波器 1. Performance comparision ...
Kalman filter algorithm consists of two stages: prediction and update Data Preparation/Scaling Standardization (Z-score Normalization) Standardize the training set using the training set means and standard deviations to prevent data leakage Subtract mean, divide standard deviation ...