Multi-rate cubature Kalman filter based data fusion method with residual compensation to adapt to sampling rate discrepancy in attitude measurement system

2017 ◽  
Vol 88 (8) ◽  
pp. 085002 ◽  
Author(s):  
Xiaoting Guo ◽  
Changku Sun ◽  
Peng Wang
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Measurement System ◽  
Sampling Rate ◽  
Attitude Measurement ◽  
Fusion Method ◽  
Cubature Kalman Filter

A Novel Data Fusion Method Based on Signal's Reliability

2013 ◽  
Vol 321-324 ◽  
pp. 561-567 ◽  
Author(s):  
Ming Xiao ◽  
Liang Pan ◽  
Yan Long Bu ◽  
Li Li Wan
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Optimal Estimation ◽  
Minimum Variance ◽  
Experimental Results ◽  
Fusion Method ◽  
One Dimensional ◽  
The One ◽  
Fusion Scheme ◽  
Better Than

The traditional Kalman filter is able to obtain the optimal estimation of the estimated signals. However, it fails to consider their reliability. In real applications, the estimated signals may include outliers. Fortunately, we are able to know the reliability of the signals transcendentally. In this paper, we derive the one-dimensional data fusion formulas based on signals reliability which is according to minimum variance restriction. Furthermore, a corresponding data fusion scheme is proposed. Experimental results show the propose data fusion method performs much better than traditional methods.

scholarly journals Comment on “Synergetic use of IASI and TROPOMI space borne sensors for generating a tropospheric methane profile product”

2021 ◽  
Author(s):  
Simone Ceccherini
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Covariance Matrices ◽  
Complete Data ◽  
Fusion Method ◽  
Error Covariance ◽  
Noise Error

Abstract. A great interest is growing about methods that combine measurements from two or more instruments that observe the same species either in different spectral regions or with different geometries. Recently, a method based on the Kalman filter has been proposed to combine IASI and TROPOMI methane products. We show that this method is equivalent to the Complete Data Fusion method. Therefore, the choice between these two methods is driven only by the advantages of the different implementations. From the comparison of the two methods a generalization of the Complete Data Fusion formula, which is valid also in the case that the noise error covariance matrices of the fused products are singular, is derived.

scholarly journals Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huadong Wang ◽  
Shi Dong
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Observational Data ◽  
Measurement System ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Multiple Sensors ◽  
Multisensor Data

In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

scholarly journals Square-Root Cubature Kalman Filter Based on H∞ Filter for Attitude Measurement of High-Spinning Aircraft

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ping-an Zhang ◽  
Wei Wang ◽  
Min Gao ◽  
Yi Wang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Euler Angle ◽  
Positive Semidefinite ◽  
Measurement Model ◽  
The State ◽  
Measurement Noise ◽  
Attitude Measurement ◽  
Square Root ◽  
Cubature Kalman Filter

A novel H∞ filter called square-root cubature H∞ Kalman filter is proposed for attitude measurement of high-spinning aircraft. In this method, a combined measurement model of three-axis geomagnetic sensor and gyroscope is used, and the Euler angle algorithm model is used to reduce the state dimension and linearize the state equation, which can reduce the amount of calculation. Simultaneously, the method can be applied to the case of measurement noise uncertainty. By continuously modifying the error limiting parameters to update the measurement noise estimation, the filtering accuracy and robustness can be improved. The square-root forms enjoy a consistently improved numerical stability because all the resulting covariance matrices by QR decomposition are guaranteed to stay positive semidefinite. The algorithm is applied to the simulation experiment of attitude measurement with the combination of geomagnetic sensor and gyroscope and compared with the results of Unscented Kalman filter, cubature Kalman filter, square root cubature Kalman filter, and singular value decomposition cubature Kalman filter, which proves the effectiveness and superiority of the algorithm.

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