Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion

2001 ◽  
Vol 37 (1) ◽  
pp. 273-279 ◽  
Author(s):  
Q. Gan ◽  
C.J. Harris
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Multisensor Data Fusion ◽  
Fusion Methods ◽  
Multisensor Data

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.

Classification Moving Vehicle Based on Multisensor Data Using Fusion of Multi-Class SVMs Methods

2014 ◽  
Vol 945-949 ◽  
pp. 1978-1981
Author(s):  
Xiao Yun Chen ◽  
Xian Fu Chen ◽  
Shao Quan Zhang ◽  
Wen Bin Zhang
Keyword(s):  
Data Fusion ◽  
Data Sources ◽  
Moving Vehicle ◽  
Multisensor Data Fusion ◽  
Fusion Methods ◽  
Time Histories ◽  
Single Data ◽  
The Individual ◽  
Fusion Approach ◽  
Multisensor Data

In this paper, we propose a special Multi-class SVMs (MSVM) data fusion strategy which is applied to classify vehicle based on multiple pavement structural strain time histories. The centralized and distributed fusion strategies are applied to combine information from several data sources. In the centralized strategy, all information from several data sources is centralized and combined to construct an input space. Then a MSVM classifier is trained. In distributed schemes, the individual data sources are processed separately and modeled by using the MSVM. Then new data fusion strategies are used to combine the information from the individual MSVM to acquire the final classification outputs. Two popular Multi-class SVMs algorithms (One-against-all OAA, One-against-one OAO) are used to construct classifier based on aforementioned two fusion strategies, respectively. The results are compared between SVM-based fusion approach and single data source SVM using two MSVM algorithms, respectively. The result shows this SVM-based fusion approach significantly improves the results of classification accuracy and robustness. The proposed Multisensor data fusion methods can also be applied in other fields.

Sign in / Sign up

Export Citation Format

Share Document