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.

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.

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.

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