Data Processing and Error Analysis of Phased-Array Doppler Sonar Velocity System

2011 ◽  
Vol 204-210 ◽  
pp. 1423-1426
Author(s):  
Kai Zhang ◽  
Kun Shang
Keyword(s):  
Error Analysis ◽  
Data Processing ◽  
Phased Array ◽  
Measurement Precision ◽  
Dominant Factor ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Doppler Sonar ◽  
Basic Test

The basic test of the phased-array Doppler sonar velocity system made by us indicates that the positioning errors are large. From the positioning result, it can be known that compass error is the dominant factor. Therefore, to improve the measurement precision of compass and revise its course deviation is one of the keys to improve the accuracy of Doppler positioning. From the speed processing result, it shows that the positioning accuracy can be further improved through averaging the speed (smoothing).

Phased array Doppler sonar transducer

1987 ◽  
Vol 82 (4) ◽  
pp. 1469-1469
Author(s):  
Robert L. Simmons ◽  
Clifton M. Wyant
Keyword(s):  
Phased Array ◽  
Doppler Sonar

scholarly journals RESEARCH OF THE METHOD OF ORTHOPHOTOMAP DECIVED FROM UAV IMAGES WITH LARGE TILT ANGLES

2021 ◽  
Vol 6 ◽  
pp. 12-17
Author(s):  
Denis V. Arutyunov
Keyword(s):  
Error Analysis ◽  
Data Processing ◽  
Flow Diagram ◽  
Topographic Survey ◽  
Uav Images

The article reflects the method of orthophotomap decivetion. The flow diagram of UAV data processing in Photomod is presented. The final processing results are presented in the form of an orthophotomap. The technology for constructing point objects for further error analysis is presented. The calculation of the plotted points in Excel for further error analysis was performed. Errors based on the tolerances that are proposed in the instructions for topographic survey are analyzed.

scholarly journals Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1186
Author(s):  
Yunhong Jia ◽  
Xiaodong Zhang ◽  
Zhenchong Wang ◽  
Wei Wang
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Absolute Error ◽  
Calibration Method ◽  
Heavy Duty ◽  
Maximum Absolute Error ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Roof Bolter ◽  
Learning Machine

Accurate positioning of an airborne heavy-duty mechanical arm in coal mine, such as a roof bolter, is important for the efficiency and safety of coal mining. Its positioning accuracy is affected not only by geometric errors but also by nongeometric errors such as link and joint compliance. In this paper, a novel calibration method based on error limited genetic algorithm (ELGA) and regularized extreme learning machine (RELM) is proposed to improve the positioning accuracy of a roof bolter. To achieve the improvement, the ELGA is firstly implemented to identify the geometric parameters of the roof bolter’s kinematics model. Then, the residual positioning errors caused by nongeometric facts are compensated with the regularized extreme learning machine (RELM) network. Experiments were carried out to validate the proposed calibration method. The experimental results show that the root mean square error (RMSE) and the mean absolute error (MAE) between the actual mast end position and the nominal mast end position are reduced by more than 78.23%. It also shows the maximum absolute error (MAXE) between the actual mast end position and the nominal mast end position is reduced by more than 58.72% in the three directions of Cartesian coordinate system.

Sign in / Sign up

Export Citation Format

Share Document