scholarly journals Study on GPS/INS System Using Novel Filtering Methods for Vessel Attitude Determination

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiyuan Chen ◽  
Chong Shen ◽  
Yuefang Zhao
Keyword(s):  
Data Fusion ◽  
Particle Filter ◽  
Attitude Determination ◽  
Genetic Operators ◽  
Fusion Algorithm ◽  
Computation Efficiency ◽  
Consumption Cost ◽  
Using Data ◽  
Improve Attitude

Any vehicle such as vessel has three attitude parameters, which are mostly defined as pitch, roll, and heading from true north. In hydrographic surveying, determination of these parameters by using GPS or INS technologies is essential for the requirements of vehicle measurements. Recently, integration of GPS/INS by using data fusion algorithm became more and more popular. Therefore, the data fusion algorithm plays an important role in vehicle attitude determination. To improve attitude determination accuracy and efficiency, two improved data fusion algorithms are presented, which are extended Kalman particle filter (EKPF) and genetic particle filter (GPF). EKPF algorithm combines particle filter (PF) with the extended Kalman filter (EKF) to avoid sample impoverishment during the resampling process. GPF is based on genetic algorithm and PF; several genetic operators such as selection, crossover, and mutation are adopted to optimize the resampling process of PF, which can not only reduce the particle impoverishment but also improve the computation efficiency. The performances of the system based on the two proposed algorithms are analyzed and compared with traditional KF. Simulation results show that, comprehensively considering the determination accuracy and consumption cost, the performance of the proposed GPF is better than EKPF and traditional KF.

GMSPPF for Magnetic Ellipsoid Tracking with a Magnetic Gradiometer Tensor

2013 ◽  
Vol 779-780 ◽  
pp. 1789-1792
Author(s):  
Zhi Dong Wu ◽  
Sui Hua Zhou ◽  
Hong Xing Zhang
Keyword(s):  
Particle Filter ◽  
Magnetic Moment ◽  
Target Position ◽  
Gaussian Mixture ◽  
State Density ◽  
Point Particle ◽  
Unknown Parameters ◽  
Sigma Point ◽  
Using Data

Magnetic ellipsoid tracking problem is characterized by high nonlinearity. In this study, the determination of target position, magnetic moment, and velocity is formulated as a Bayesian estimation problem for dynamic systems, a recursive approach is proposed to estimate the trajectory and magnetic moment component of the target using data collected with a magnetic gradiometer tensor. Particle filter provides a solution to this problem. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses Gaussian mixed mode to represent the posterior state density of the unknown parameters, which is named as Gaussian mixture sigma point particle filters(GMSPPF). The performance of the proposed method has been evaluated through simulation experiment. The results indicate that the method has achieved the magnetic ellipsoid tracking and GMSPPF has better estimation performance and less computational complexity than other related algorithms.

Study on Fast Recognition of Biotoxins and Biological Modifiers Using Data Fusion Algorithm

2020 ◽  
Vol 48 (10) ◽  
pp. 1343-1350
Author(s):  
Di-Na NAN ◽  
Wei-Wei LIU ◽  
Wen-Xiang FU ◽  
Bao-Qiang LI ◽  
Jing-Lin KONG
Keyword(s):  
Data Fusion ◽  
Fusion Algorithm ◽  
Fast Recognition ◽  
Using Data

Navigation Sensor Data Fusion for the AUV Remus

2001 ◽  
Vol 38 (01) ◽  
pp. 65-69
Author(s):  
Thomas F. Fulton ◽  
Christopher J. Cassidy
Keyword(s):  
Data Fusion ◽  
Autonomous Underwater Vehicle ◽  
Low Cost ◽  
Field Trials ◽  
Sensor Data ◽  
Doppler Velocity ◽  
Fusion Algorithm ◽  
Sensor Data Fusion ◽  
Acoustic Navigation ◽  
Using Data

The development of a navigation sensor data fusion algorithm for the autonomous underwater vehicle (AUV) Remus is described. Remus is a small, low-cost AUV designed and built at the Ocean Systems Laboratory of the Woods Hole Oceanographic Institute. The navigation sensors for Remus include an acoustic navigation system, a Doppler velocity sonar, and a compass. The data from these sensors are integrated in an extended Kalman filter, with the objective of producing a more accurate vehicle track. Postprocessing results using data from two recent field trials are presented.

Initial Attitude Determination of Aerial Platform Based on MIMU

2014 ◽  
Vol 568-570 ◽  
pp. 964-969 ◽  
Author(s):  
Xiao Jing Du ◽  
Xiao Yang Lan ◽  
Jun Yi Zhai
Keyword(s):  
Attitude Determination ◽  
Self Determination ◽  
Test Results ◽  
Fusion Algorithm ◽  
Initial Deviation ◽  
Filter Structure ◽  
Initial Attitude ◽  
Aerial Platform

Aiming at the problem of initial attitude determination of aerial platform, a low computational attitude fusion algorithm is proposed based on MIMU. Attitude kinematic equations described by quaternions are developed and the filter structure used in this paper is designed. Real fly test results show that: at different initial deviation, the alignment precision of roll and pitch angles are within 1 °, attitude errors converge to 5 °or less within 12s, which meets the requirements of the aerial platform attitude fast self-determination. The filter algorithm has a small amount of calculation and is easy to be realized.

Rapid determination of farinograph parameters of wheat flour using data fusion and a forward interval variable selection algorithm

2017 ◽  
Vol 9 (45) ◽  
pp. 6341-6348 ◽  
Author(s):  
Jia Chen ◽  
Fayin Ye ◽  
Guohua Zhao
Keyword(s):  
Variable Selection ◽  
Data Fusion ◽  
Wheat Flour ◽  
Rapid Determination ◽  
Selection Algorithm ◽  
Interval Variable ◽  
Using Data

A forward interval variable selection algorithm combined with data fusion was developed to determine farinograph parameters of wheat flour.

scholarly journals Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

Energies ◽  
2015 ◽  
Vol 8 (12) ◽  
pp. 13911-13927 ◽  
Author(s):  
Feng Lu ◽  
Yafan Wang ◽  
Jinquan Huang ◽  
Yihuan Huang
Keyword(s):  
Data Fusion ◽  
Particle Filter ◽  
Gas Turbine ◽  
Transient Performance ◽  
Performance Tracking ◽  
Using Data

Using data fusion of DMARS-R-IMU and GPS data for improving attitude determination accuracy

2016 ◽  
Author(s):  
Josef Ettl ◽  
Dong Kim ◽  
Alexander P. Schmidt ◽  
John Turner
Keyword(s):  
Data Fusion ◽  
Attitude Determination ◽  
Gps Data ◽  
Using Data

Real-time detection of moving objects in a video sequence by using data fusion algorithm

2018 ◽  
Vol 41 (3) ◽  
pp. 793-804 ◽  
Author(s):  
Chao Tang ◽  
Huosheng Hu ◽  
Miaohui Zhang ◽  
Wen-Jian Wang ◽  
Xiao-Feng Wang ◽  
...  
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Video Sequence ◽  
Moving Objects ◽  
Motion Vector ◽  
Visual Surveillance ◽  
Moving Object ◽  
Fusion Algorithm ◽  
Illumination Variation ◽  
Using Data

The moving object detection and tracking technology has been widely deployed in visual surveillance for security, which is, however, an extremely challenge to achieve real-time performance owing to environmental noise, background complexity and illumination variation. This paper proposes a novel data fusion approach to attack this problem, which combines an entropy-based Canny (EC) operator with the local and global optical flow (LGOF) method, namely EC-LGOF. Its operation contains four steps. The EC operator firstly computes the contour of moving objects in a video sequence, and the LGOF method then establishes the motion vector field. Thirdly, the minimum error threshold selection (METS) method is employed to distinguish the moving object from the background. Finally, edge information fuses temporal information concerning the optic flow to label the moving objects. Experiments are conducted and the results are given to show the feasibility and effectiveness of the proposed method.

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