Effects of Spatial Data Density, Sensor Noise and Position Errors on UXO and Clutter Target Parameters from Inversions of ALLTEM Data

2008 ◽  
Author(s):  
David L. Wright ◽  
Theodore H. Asch ◽  
Craig W. Moulton ◽  
Trevor P. Irons ◽  
Misac N. Nabighian
Keyword(s):  
Spatial Data ◽  
Sensor Noise ◽  
Position Errors ◽  
Data Density

Effects Of Spatial Data Density, Sensor Noise And Position Errors On Uxo And Clutter Target Parameters From Inversions Of Alltem Data

2008 ◽  
Author(s):  
David L. Wright ◽  
Theodore H. Asch ◽  
Craig W. Moulton ◽  
Trevor P. Irons ◽  
Misac N. Nabighian
Keyword(s):  
Spatial Data ◽  
Sensor Noise ◽  
Position Errors ◽  
Data Density

Wavelet Analysis For Improving INS and INS/DGPS Navigation Accuracy

2005 ◽  
Vol 58 (1) ◽  
pp. 119-134 ◽  
Author(s):  
Sameh Nassar ◽  
Naser El-Sheimy
Keyword(s):  
Navigation System ◽  
Inertial Navigation ◽  
Frequency Noise ◽  
Data Sets ◽  
Sensor Noise ◽  
Noise Component ◽  
Position Errors ◽  
Wavelet Techniques ◽  
Inertial Measurements ◽  
Navigation Accuracy

The integration of the Global Positioning System (DGPS) with an Inertial Navigation System (INS) has been implemented for several years. In an integrated INS/DGPS system, the DGPS provides positions while the INS provides attitudes. In case of DGPS outages (signal blockages), the INS is used for positioning until the DGPS signals are available again. One of the major issues that limit the INS accuracy, as a stand-alone navigation system, is the level of sensor noise. The problem with inertial data is that the required signal is buried into a large window of high frequency noise. If such noise component could be removed, the overall inertial navigation accuracy is expected to improve considerably. The INS sensor outputs contain actual vehicle motion and sensor noise. Therefore, the resulting position errors are proportional to the existing sensor noise and vehicle vibrations. In this paper, wavelet techniques are applied for de-noising the inertial measurements to minimize the undesirable effects of sensor noise and other disturbances. To test the efficiency of inertial data de-noising, two road vehicle INS/DGPS data sets are utilized. Compared to the obtained position errors using the original inertial measurements, the results showed that the positioning performance using de-noised data improves by 34%–63%.

scholarly journals On bandwidth choice for spatial data density estimation

2020 ◽  
Vol 82 (3) ◽  
pp. 817-840
Author(s):  
Zhenyu Jiang ◽  
Nengxiang Ling ◽  
Zudi Lu ◽  
Dag Tj⊘stheim ◽  
Qiang Zhang
Keyword(s):  
Density Estimation ◽  
Spatial Data ◽  
Data Density ◽  
Data Density Estimation

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

An empirical analysis of town-forming enterprises' corporate social responsibility: Evidence from the Volga Federal District

2020 ◽  
Vol 18 (10) ◽  
pp. 1894-1909
Author(s):  
I.R. Badykova
Keyword(s):  
Regression Analysis ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Empirical Analysis ◽  
Spatial Data ◽  
Univariate Analysis ◽  
Federal District ◽  
Current Situation ◽  
Economic Situation ◽  
Corporate Social

Subject. This article explores the determinants of social responsibility of backbone enterprises. Objectives. The article aims to investigate the relationships between the socio-economic situation of the monotown where the backbone company operates, and corporate social responsibility (CSR). Methods. For the study, I used a regression analysis and univariate analysis of spatial data. The rating estimates calculated using an original methodology are used as a CSR proxy (dependent variable). Results. Presenting information about the current situation of backbone enterprises and monotowns in Russia, the article reveals the existence of relationships between the backbone enterprise's affiliation to a monotown with a certain socio-economic situation and the level of corporate social responsibility. Conclusions. The situation of the backbone companies is likely to deteriorate. Increasing the level of social responsibility during a crisis seems unlikely.

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