scholarly journals A New View of Multisensor Data Fusion: Research on Generalized Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Guo Chen ◽  
Zhigui Liu ◽  
Guang Yu ◽  
Jianhong Liang
Keyword(s):  
Data Fusion ◽  
Heterogeneous Data ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Symbolic Computing ◽  
Multisensor Data Fusion ◽  
Definition Of ◽  
Future Work ◽  
Multisensor Data

Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

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.

Multisensor data fusion model for multitarget tracking

1990 ◽  
Author(s):  
Hamid Rasoulian ◽  
Wiley E. Thompson ◽  
Louis F. Kazda ◽  
Ramon Parra-Loera
Keyword(s):  
Data Fusion ◽  
Multitarget Tracking ◽  
Fusion Model ◽  
Multisensor Data Fusion ◽  
Multisensor Data

scholarly journals Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Waleed A. Abdulhafiz ◽  
Alaa Khamis
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Data Uncertainty ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Combining Data ◽  
Bayesian Fusion ◽  
The Individual ◽  
Multisensor Data

Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data.

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