MULTISENSOR DATA FUSION TECHNOLOGY

The data fusion from multiple sensors is one of the areas and technologies of interest to national defense (Ministry of Defense of Brazil). Approximately 20 years ago, several methods of data fusion were introduced. Such methods are generally related to the defense sector, but data fusion can also be applied in other areas such as medical diagnostics, industry, etc. A simple way to explain its operation is to state that this technology combines information from several sensors in order to produce other, more accurate information.

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Monitoring and Analysis of Physical Exercise Effects Based on Multisensor Information Fusion

Journal of Sensors ◽

10.1155/2022/4199985 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Xinliang Zhou ◽

Shantian Wen

Keyword(s):

Physical Exercise ◽

Data Fusion ◽

Information Fusion ◽

Development Process ◽

System Optimization ◽

Physiological Parameters ◽

Fusion Technology ◽

Multisensor Data Fusion ◽

Multiple Sensors ◽

Multisensor Information Fusion

In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.

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Water Dragon Boat Training Monitoring System Based on Multisensor Data Fusion Technology

Journal of Sensors ◽

10.1155/2021/4183881 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Heng Shen

Keyword(s):

Data Fusion ◽

Monitoring System ◽

Estimation Method ◽

Fusion Rule ◽

Fusion Technology ◽

Fusion Rules ◽

Multisensor Data Fusion ◽

Dragon Boat ◽

Multisensor Data ◽

Integration Rules

With the development of science and technology, a variety of electronic devices have entered our lives, making our lives more intelligent and making our work more effective. This article is aimed at studying the application of multisensor data fusion technology to the water dragon boat training monitoring system. In that case, we can analyze the various physical indicators of dragon boat athletes based on the data reflected by these sensors, when they can reach their physical limits and can perform in the best state to obtain the best results. The sensor is used to decompose the relevant data of each part of the athlete’s limbs. This step is based on the image and understands the maximum value of the data to adjust the training goal. This article proposes some data fusion algorithms, using Kalman filter method, Bayesian estimation method, and DS evidence theory algorithm to compare data fusion systems, through the comparison to find the best fusion accuracy, and then get the most suitable method is then applied to this water dragon boat monitoring system to enhance the training efficiency of dragon boat athletes. The experimental results in this paper show that when the value of the parameter increases from 0.97 to 2.5, the average classification accuracy of the k -NN classifier decreases from 0.97 to 0.4, and the accuracy of the fusion results of the three fusion rules is also reduced correspondingly, but in this paper proposed, RP fusion rule still has better performance than the other two fusion rules. When the classifier is k -NN, the three fusion rules increase with the number of sensors, and the accuracy of the fusion results is correspondingly improved. However, the final fusion accuracy obtained by the RP fusion rule proposed in this paper is always better than NB integration rules, and WMV integration rules are higher. Through these analyses, a training program that is most suitable for dragon boat athletes can be worked out, so that the athletes will not be useless. Multisensor data fusion technology brings great convenience to water dragon boat training and can provide more reasonable and accurate data to explore a practical way on the basis of ensuring the safety of personnel.

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

Mathematical Problems in Engineering ◽

10.1155/2015/854085 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 1

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.

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