A genetic algorithm for selection of noisy sensor data in multisensor data fusion

1997 ◽  
Author(s):  
A. Ali Khan ◽  
M.A. Zohdy
Keyword(s):  
Genetic Algorithm ◽  
Data Fusion ◽  
Sensor Data ◽  
Multisensor Data Fusion ◽  
Multisensor Data ◽  
Selection Of

Based on Multi-Sensor Data Fusion Operating Machinery Velocity Monitoring System

2012 ◽  
Vol 241-244 ◽  
pp. 993-997
Author(s):  
Xiu Ying Xu ◽  
Cao Jun Huang
Keyword(s):  
Data Fusion ◽  
High Reliability ◽  
System Structure ◽  
Sensor Data ◽  
High Price ◽  
Positioning System ◽  
Multisensor Data Fusion ◽  
Speed Measurement ◽  
Multi Sensor Data Fusion ◽  
Multisensor Data

Against the shortcomings such as high price and low precision , big error leading with land wheel exist shift phenomenon in traditional tachometry, raised that the idea of using multisensor data fusion handles operating machinery speed in the field, introduced a fusion progress system structure and applied the kalman filter data fusion algorithm.This system which is based on ATS665 and GPS and mcu MSP430F1121 can work in operating machinery speed measurement and in car navigation and positioning system .The above_mentioned determines the high reliability,high cost perfomance and using easily.

Multisensor data fusion based on genetic algorithm

1996 ◽  
Author(s):  
Feng Liu ◽  
Xinju Cai ◽  
Bao Shi ◽  
Yue Wang
Keyword(s):  
Genetic Algorithm ◽  
Data Fusion ◽  
Multisensor Data Fusion ◽  
Multisensor Data

scholarly journals Common Data Fusion Framework: An open-source Common Data Fusion Framework for space robotics

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091176
Author(s):  
Raul Dominguez ◽  
Mark Post ◽  
Alexander Fabisch ◽  
Romain Michalec ◽  
Vincent Bissonnette ◽  
...  
Keyword(s):  
Data Fusion ◽  
Autonomous Systems ◽  
Vital Role ◽  
Sensor Data ◽  
Space Robotics ◽  
Multisensor Data Fusion ◽  
Framework Approach ◽  
The Common ◽  
Fusion Framework ◽  
Multisensor Data

Multisensor data fusion plays a vital role in providing autonomous systems with environmental information crucial for reliable functioning. In this article, we summarize the modular structure of the newly developed and released Common Data Fusion Framework and explain how it is used. Sensor data are registered and fused within the Common Data Fusion Framework to produce comprehensive 3D environment representations and pose estimations. The proposed software components to model this process in a reusable manner are presented through a complete overview of the framework, then the provided data fusion algorithms are listed, and through the case of 3D reconstruction from 2D images, the Common Data Fusion Framework approach is exemplified. The Common Data Fusion Framework has been deployed and tested in various scenarios that include robots performing operations of planetary rover exploration and tracking of orbiting satellites.

scholarly journals Human Activity Registration Using Multisensor Data Fusion

2015 ◽  
Vol 15 (7) ◽  
pp. 99-108 ◽  
Author(s):  
Kiril Alexiev ◽  
Georgi Shishkov ◽  
Nevena Popova
Keyword(s):  
Data Fusion ◽  
Human Activity ◽  
Smart Phone ◽  
Smart Phones ◽  
Sensor Data ◽  
Processing Unit ◽  
Functional Characteristics ◽  
Multisensor Data Fusion ◽  
Data Source ◽  
Multisensor Data

Abstract The paper discusses the feasibility of using smart phone devices for human activity registration and analysis. The functional characteristics of the smart phones and their permanent connectivity allow them to serve as a measurement lab and processing unit. An example of using the smart phones as a sensor data source is described, and the corresponding algorithm and results are given. The possible problems are listed and commented.

scholarly journals Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongda Zhang ◽  
Ting Zhang
Keyword(s):  
Parallel Processing ◽  
Data Fusion ◽  
Inertial Sensor ◽  
Processing Method ◽  
Feature Subset Selection ◽  
Sensor Data ◽  
Feature Subset ◽  
Vertical Projection ◽  
Multisensor Data Fusion ◽  
Multisensor Data

Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.

Multisensor Data Fusion Based on Rough Set and ART2 Network

2012 ◽  
Vol 241-244 ◽  
pp. 912-915
Author(s):  
Xiao Hui Chen ◽  
Hui Liu
Keyword(s):  
Decision Making ◽  
Data Fusion ◽  
Rough Set ◽  
Sample Space ◽  
Sensor Data ◽  
Knowledge Reduction ◽  
Multisensor Data Fusion ◽  
Distribution Rules ◽  
Sensor Clustering ◽  
Multisensor Data

A difficulty of multisensor data fusion lies in the switching of the state of sensor clusters. That is, which direction should the sensor data been fused into at a given moment? In this paper, firstly, the rough set was used for access of knowledge. The typical clustering distributions of 54 sensors within one day were regarded as sample space for the decision-making table of the "data - fusion distribution". Next, based on the method of knowledge reduction using rough set, the redundant properties and samples of data in one month were removed. Then, the ART2 network was applied for clustering and analyzing, and the distribution rules of multisensor data fusion are formed. The experiment results show that the model is efficient in classification and rapid in sensor clustering distribution decide.

scholarly journals Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Daohua Pan ◽  
Hongwei Liu ◽  
Dongming Qu ◽  
Zhan Zhang
Keyword(s):  
Data Fusion ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Support Vector ◽  
Training Set ◽  
Multisensor Data Fusion ◽  
Intelligent Devices ◽  
Multisensor Data

Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.

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.

Orthogonal Analysis of Multisensor Data Fusion for Improved Quality Control

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Peng Wang ◽  
Zhaoyan Fan ◽  
David O. Kazmer ◽  
Robert X. Gao
Keyword(s):  
Quality Control ◽  
Data Fusion ◽  
Dimensional Space ◽  
Sensor Data ◽  
Polymer Injection ◽  
Multisensor Data Fusion ◽  
Part Quality ◽  
Manufacturing Process Control ◽  
Using Data ◽  
Multisensor Data

Multisensor data fusion can enable comprehensive representation of manufacturing processes, thereby contributing to improved part quality control. The effectiveness of data fusion depends on the nature of the input data. This paper investigates orthogonality as a measure for the effectiveness of data fusion, with the goal to maximize data correlation with part quality toward manufacturing process control. By decomposing sensor data into a lifted-dimensional space, contribution from each of the sensors for quantifying part quality is revealed by the corresponding projection vector. Performance evaluation using data measured from polymer injection molding confirmed the effectiveness of the developed technique.

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