scholarly journals Underwater mobile gravity measurement data processing using continuous-discrete Kalman filter

AIP Advances ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 085104
Author(s):  
Zhiqiang Zhang ◽  
Zhongle Liu ◽  
Hongxin Zhang
Keyword(s):  
Kalman Filter ◽  
Data Processing ◽  
Measurement Data ◽  
Gravity Measurement

Absorbed Radiation Heat Flux Sensors for Orbital Spacecraft. Algorithms for Measurement Data Processing

2021 ◽  
Vol 30 (3) ◽  
pp. 383-403
Author(s):  
A. V. Nenarokomov ◽  
D. L. Reviznikov ◽  
D. A. Neverova ◽  
E. V. Chebakov ◽  
A. V. Morzhukhina ◽  
...  
Keyword(s):  
Heat Flux ◽  
Data Processing ◽  
Measurement Data ◽  
Radiation Heat ◽  
Radiation Heat Flux ◽  
Heat Flux Sensors ◽  
Orbital Spacecraft

scholarly journals Selected algorithms for measurement data processing in impulse-radar-based system for monitoring of human movements

2016 ◽  
Vol 772 ◽  
pp. 012057 ◽  
Author(s):  
Andrzej Miękina ◽  
Jakub Wagner ◽  
Paweł Mazurek ◽  
Roman Z. Morawski
Keyword(s):  
Data Processing ◽  
Measurement Data ◽  
Human Movements

scholarly journals Information processing using the Kalman filter in Matlab Simulink

2021 ◽  
pp. 49-62
Author(s):  
Vladimir F. Telezhkin ◽  
◽  
Bekhruz B. Saidov ◽  
Keyword(s):  
Kalman Filter ◽  
Control Systems ◽  
Measurement Data ◽  
Control Device ◽  
Software Systems ◽  
Automated Control ◽  
Matlab Simulink ◽  
Software Algorithms ◽  
And Performance ◽  
Filter Noise

In this paper, we investigate the problem of improving data quality using the Kalman filter in Matlab Simulink. Recently, this filter has become one of the most common algorithms for filtering and processing data in the implementation of control systems (including automated control systems) and the creation of software systems for digital filtering from noise and interference, for example, speech signals. It is also widely used in many fields of science and technology. Due to its simplicity and efficiency, it can be found in GPS receivers, in devices for processing sensor readings for various purposes, etc. It is known that one of the important tasks that should be solved in systems for processing sensor readings is the ability to detect and filter noise. Sensor noise leads to unstable measurement data. This, of course, ultimately leads to a decrease in the accuracy and performance of the control device. One of the methods that can be used to solve the problem of optimal filtering is the development of cybernetic algorithms based on the Kalman and Wiener filters. The filtering process can be carried out in two forms, namely: hardware and software algorithms. Hardware filtering can be built electronically. However, it is less efficient as it requires additional circuitry in the system. To overcome this obstacle, you can use filtering in the form of programming algorithms in a single method. In addition to the fact that it does not require electronic hardware circuitry, the filtering performed is even more accurate because it uses a computational process. The paper analyzes the results of applying the Kalman filter to eliminate errors when measuring the coordinates of the tracked target, to obtain a "smoothed" trajectory and shows the results of the filter development process when processing an electrocardiogram. The development of the Kalman filter algorithm is based on the procedure of recursive assessment of the measured state of the research object.

Visual Processing of the River Cross-Sectional Data

2012 ◽  
Vol 204-208 ◽  
pp. 2726-2730
Author(s):  
Qi Qing Duan ◽  
Rui Hai Wu
Keyword(s):  
Data Processing ◽  
Cross Section ◽  
Visual Processing ◽  
Measurement Data ◽  
Measurement Process ◽  
Cross Section Data ◽  
Section Data ◽  
Section Line ◽  
Straight Line ◽  
The Cross

The cross-section of Hydraulic engineering (river, embankment) is a kind of cross section which is always perpendicular to the river direction. Section line is a straight line which is created by connecting two endpoint of the section. Cross-section measurements is that collecting a coordinate point (X, Y, H) on the section line every a certain distance. Field measurement, due to the influence of the external environment, especially when measured in the river, is difficult to ensure that the location of the measurement point exactly on the straight line shown in the section. The reason is that tracking ship traveling along with the section will be impacted by the water, resulting in the offset along the flow direction. Therefore we must to constantly adjust the direction of travel in the measurement process. For which the measurement data should be processed. So it is necessary to deal with the measurement data, and the idea of visual data was proposed in the paper, which is easier to analyze the accuracy of the measurement data. The BUFFER analysis method was used in the data processing, which effectively removed measurement invalid point that far away from the cross-section in measurement and improved the accuracy of the cross-section data processing. On the other hand, the effective pedal point coordinates was used in the calculation of the plane location of cross-section point. The coordinate which can make the cross-section data more realistic and different from the translation of point and uniform distribution algorithm closeted to the effective point of measurement. The method that the elevation of pedal point on the cross section calculated using the distance weighted interpolation method has been applied in the measurement process of several rivers. It is proved in practice that the method got good results and achieved the accuracy of the data and quality which the application sector requirements on.

Does the brain implement the Kalman filter?

2004 ◽  
Vol 27 (3) ◽  
pp. 404-405 ◽  
Author(s):  
Valeri Goussev
Keyword(s):  
Kalman Filter ◽  
Data Processing ◽  
Kalman Filtering ◽  
Brain Structures ◽  
Parameter Evaluation ◽  
Filtering Technique ◽  
Processing Techniques ◽  
The Brain

The Kalman filtering technique is considered as a part of concurrent data-processing techniques also related to detection, parameter evaluation, and identification. The adaptive properties of the filter are discussed as being related to symmetrical brain structures.

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