A Computationally Tractable Approach To Real-Time Multi-Sensor Data Fusion

1989 ◽  
Author(s):  
P. F. Easthope ◽  
E. J. G. Goodchild ◽  
S. L. Rhodes
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

scholarly journals Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 919 ◽  
Author(s):  
Hao Du ◽  
Wei Wang ◽  
Chaowen Xu ◽  
Ran Xiao ◽  
Changyin Sun
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

Study on Leak Detecting System for Oil Pipeline Based on Multi-Sensor Data Fusion Technique

2012 ◽  
Vol 220-223 ◽  
pp. 1898-1901
Author(s):  
Jie Zhang ◽  
Xiao Wang ◽  
Zhen Zhao
Keyword(s):  
Wavelet Transform ◽  
Data Fusion ◽  
Real Time ◽  
Sensor Data ◽  
Time Signal ◽  
Fusion Technology ◽  
Transform Method ◽  
Oil Pipeline ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

In this paper, based on the wavelet transform method, the multi-sensor data fusion technology is adopted to solve some key problems in pipeline leak detection system. Compared with the traditional filtering method, the wavelet transform method can better remove the pipeline leakage signal noise. When the corresponding wavelet is selected, after FPGA realized, the method has the advantages of fast speed, arbitrarily data width set, which can better meet the needs of real-time signal processing requirements of pipeline leak. At the same time, VHDL language has the characteristics of portability, greater generality. When multi-sensor fusion technology is used in the paper, the relation, correlation, estimation and integrated processing is done in turn to the information from multiple data sources, the system can achieve better detection performance than a system using the single sensor, so as to form a more complete, reliable pipeline real time detecting conclusion.

scholarly journals Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4381 ◽  
Author(s):  
E. García Plaza ◽  
P. Núñez López ◽  
E. Beamud González
Keyword(s):  
Quality Control ◽  
Data Fusion ◽  
Real Time ◽  
Surface Quality ◽  
Predictive Power ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Modeling Techniques ◽  
Signal Processing Methods

Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

Design of Safety Early Warning System Based on Multi-Sensor Data Fusion

2014 ◽  
Vol 602-605 ◽  
pp. 2276-2280
Author(s):  
Chuan Hua Li ◽  
Jing Yin ◽  
Xiao Fang Yang
Keyword(s):  
Fuzzy Control ◽  
Data Fusion ◽  
Real Time ◽  
Monitoring System ◽  
Safety Monitoring ◽  
Sensor Data ◽  
Time Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Safety Monitoring System

A typical safety monitoring system architecture with basic functions including data collection, parameter setting, data processing and backup, warning judgment and processing is proposed based on analysis of potential safety hazards. With the theories such as T-S model, domain processing and fuzzy control, a fuzzy control model based on multi-sensor data fusion is established, and a safety monitoring system software is developed. Test result shows that the software supports both analog and real-time data, and can perform real-time monitoring of safety parameters like temperature and smoke with satisfactory online hazard warning performance, thus able to be applied in environmental safety monitoring.

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