scholarly journals Determination of Navigation System Positioning Accuracy Using the Reliability Method Based on Real Measurements

2021 ◽  
Vol 13 (21) ◽  
pp. 4424
Author(s):  
Mariusz Specht
Keyword(s):  
Global Positioning System ◽  
Navigation System ◽  
Classical Method ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Position Errors ◽  
Accuracy Measure ◽  
Reliability Method ◽  
Global Positioning

In navigation, the Twice the Distance Root Mean Square (2DRMS) is commonly used as a position accuracy measure. Its determination, based on statistical methods, assumes that the position errors are normally distributed and are often not reflected in actual measurements. As a result of the widespread adoption of this measure, the positioning accuracy of navigation systems is overestimated by 10–15%. In this paper, a new method is presented for determining the navigation system positioning accuracy based on a reliability model where the system’s operation and failure statistics are referred to as life and failure times. Based on real measurements, the method proposed in this article will be compared with the classical method (based on the 2DRMS measure). Real (empirical) measurements made by the principal modern navigation positioning systems were used in the analyses: Global Positioning System (GPS) (168’286 fixes), Differential Global Positioning System (DGPS) (900’000 fixes) and European Geostationary Navigation Overlay Service (EGNOS) (900’000 fixes). Research performed on real data, many of which can be considered representative, have shown that the reliability method provides a better (compared to the 2DRMS measure) estimate of navigation system positioning accuracy. Thanks to its application, it is possible to determine the position error distribution of the navigation system more precisely when compared to the classical method, as well as to indicate those applications that can be used by this system, ensuring the safety of the navigation process.

scholarly journals Method of Evaluating the Positioning System Capability for Complying with the Minimum Accuracy Requirements for the International Hydrographic Organization Orders

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3860 ◽  
Author(s):  
Specht
Keyword(s):  
Global Positioning System ◽  
Satellite System ◽  
Positioning System ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Global Positioning ◽  
Gnss Receivers ◽  
Global Navigation Satellite ◽  
Alternative Solutions

According to the IHO (International Hydrographic Organization) S-44 standard, hydrographic surveys can be carried out in four categories, the so-called orders—special, 1a, 1b, and 2—for which minimum accuracy requirements for the applied positioning system have been set out. These amount to, respectively: 2 m, 5 m, 5 m, and 20 m at a confidence level of 0.95. It is widely assumed that GNSS (Global Navigation Satellite System) network solutions with an accuracy of 2–5 cm (p = 0.95) and maritime DGPS (Differential Global Positioning System) systems with an error of 1–2 m (p = 0.95) are currently the two main positioning methods in hydrography. Other positioning systems whose positioning accuracy increases from year to year (and which may serve as alternative solutions) have been omitted. The article proposes a method that enables an assessment of any given navigation positioning system in terms of its compliance (or non-compliance) with the minimum accuracy requirements specified for hydrographic surveys. The method concerned clearly assesses whether a particular positioning system meets the accuracy requirements set out for a particular IHO order. The model was verified, taking into account both past and present research results (stationary and dynamic) derived from tests on the following systems: DGPS, EGNOS (European Geostationary Navigation Overlay Service), and multi-GNSS receivers (GPS/GLONASS/BDS/Galileo). The study confirmed that the DGPS system meets the requirements for all IHO orders and proved that the EGNOS system can currently be applied in measurements in the orders 1a, 1b, and 2. On the other hand, multi-GNSS receivers meet the requirements for order 2, while some of them meet the requirements for orders 1a and 1b as well.

Consistency analysis of global positioning system position errors with typical statistical distributions

2021 ◽  
pp. 1-18
Author(s):  
Mariusz Specht
Keyword(s):  
Normal Distribution ◽  
Global Positioning System ◽  
Position Error ◽  
Statistical Distribution ◽  
Positioning System ◽  
Statistical Distributions ◽  
Position Errors ◽  
Global Positioning ◽  
Gps System ◽  
Best Fit

Abstract Research into statistical distributions of φ, λ and two-dimensional (2D) position errors of the global positioning system (GPS) enables the evaluation of its accuracy. Based on this, the navigation applications in which the positioning system can be used are determined. However, studies of GPS accuracy indicate that the empirical φ and λ errors deviate from the typical normal distribution, significantly affecting the statistical distribution of 2D position errors. Therefore, determining the actual statistical distributions of position errors (1D and 2D) is decisive for the precision of calculating the actual accuracy of the GPS system. In this paper, based on two measurement sessions (900,000 and 237,000 fixes), the distributions of GPS position error statistics in both 1D and 2D space are analysed. Statistical distribution measures are determined using statistical tests, the hypothesis on the normal distribution of φ and λ errors is verified, and the consistency of GPS position errors with commonly used statistical distributions is assessed together with finding the best fit. Research has shown that φ and λ errors for the GPS system are normally distributed. It is proven that φ and λ errors are more concentrated around the central value than in a typical normal distribution (positive kurtosis) with a low value of asymmetry. Moreover, φ errors are clearly more concentrated than λ errors. This results in larger standard deviation values for φ errors than λ errors. The differences in both values were 25–39%. Regarding the 2D position error, it should be noted that the value of twice the distance root mean square (2DRMS) is about 10–14% greater than the value of R95. In addition, studies show that statistical distributions such as beta, gamma, lognormal and Weibull are the best fit for 2D position errors in the GPS system.

scholarly journals An autonomous navigational system using GPS and computer vision for futuristic road traffic

2022 ◽  
Vol 12 (1) ◽  
pp. 179
Author(s):  
Prabha Ramasamy ◽  
Mohan Kabadi
Keyword(s):  
Computer Vision ◽  
Global Positioning System ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Road Traffic ◽  
Positioning System ◽  
Traffic System ◽  
Global Positioning ◽  
Navigational System

Navigational service is one of the most essential dependency towards any transport system and at present, there are various revolutionary approaches that has contributed towards its improvement. This paper has reviewed the global positioning system (GPS) and computer vision based navigational system and found that there is a large gap between the actual demand of navigation and what currently exists. Therefore, the proposed study discusses about a novel framework of an autonomous navigation system that uses GPS as well as computer vision considering the case study of futuristic road traffic system. An analytical model is built up where the geo-referenced data from GPS is integrated with the signals captured from the visual sensors are considered to implement this concept. The simulated outcome of the study shows that proposed study offers enhanced accuracy as well as faster processing in contrast to existing approaches.

scholarly journals New Global Referencing Approach in a Camera-LCD Micro Positioning System

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2118
Author(s):  
Óscar de Francisco Ortiz ◽  
Irene Ortiz ◽  
Antonio Bueno
Keyword(s):  
Global Positioning System ◽  
New Technology ◽  
Positioning System ◽  
New Approach ◽  
Positioning Systems ◽  
System A ◽  
Quality Product ◽  
Global Positioning ◽  
Accuracy And Repeatability ◽  
Current Systems

In any precision manufacturing process, positioning systems play a very important role in achieving a quality product. As a new approach to current systems, camera-LCD positioning systems are a new technology that can provide substantial improvements enabling better accuracy and repeatability. However, in order to provide stability to the system a global positioning system is required. This paper presents an improvement of a positioning system based on the treatment of images on an LCD in which a new algorithm with absolute reference has been implemented. The method is based on basic geometry and linear algebra applied to computer vision. The algorithm determines the spiral center using an image taken at any point. Consequently, the system constantly knows its position and does not lose its reference. Several modifications of the algorithm are proposed and compared. The simulation and test of the algorithm provide an important improvement in the reliability and stability of the positioning system providing errors of microns for the calculation of the global position used by the algorithm.

Seamless INS/GPS Integration Based on Support Vector Machines

2013 ◽  
Vol 336-338 ◽  
pp. 277-280 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Support Vector Machines ◽  
Global Positioning System ◽  
Navigation System ◽  
Superior Performance ◽  
Support Vector ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Integrated Navigation System ◽  
Vector Machines ◽  
Global Positioning

The combination of Inertial Navigation System (INS) and Global Positioning System (GPS) provides superior performance in comparison with either a stand-alone INS or GPS. However, the positioning accuracy of INS/GPS deteriorates with time in the absence of GPS signals. A least squares support vector machines (LS-SVM) regression algorithm is applied to INS/GPS integrated navigation system to bridge the GPS outages to achieve seamless navigation. In this method, LS-SVM is trained to model the errors of INS when GPS is available. Once the LS-SVM is properly trained in the training phase, its prediction can be used to correct the INS errors during GPS outages. Simulations in INS/GPS integrated navigation showed improvements in positioning accuracy when GPS outages occur.

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