scholarly journals Reliability Analysis of Network Real-Time Kinematic

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Mohammed Ouassou ◽  
Bent Natvig ◽  
Anna B. O. Jensen ◽  
Jørund I. Gåsemyr
Keyword(s):  
Real Time ◽  
Reliability Theory ◽  
Covariance Matrices ◽  
Direct Influence ◽  
System State ◽  
Sensitive Data ◽  
Position Accuracy ◽  
Real Time Kinematic ◽  
Critical Components ◽  
Different Levels

The multistate reliability theory was applied to the network real-time kinematic (NRTK) data processing chain, where the qualities of the network corrections, baseline residuals, and the associated variance-covariance matrices are considered as the system state vectors. The state vectors have direct influence on the rover receiver position accuracy. The penalized honored stochastic averaged standard deviation (PHSASD) is used to map the NRTK sensitive data, represented by the states vectors to different levels of performance. The study shows that the improvement is possible by identification of critical components in the NRTK system and implementation of some parallelism that makes the system more robust.

scholarly journals Study the capabilities of RTK Multi GNSS under forest canopy in regions of Indonesia

2019 ◽  
Vol 94 ◽  
pp. 01021 ◽  
Author(s):  
Heri Andreas ◽  
Hasanuddin Zainal Abidin ◽  
Dina Anggreni Sarsito ◽  
Dhota Pradipta
Keyword(s):  
Real Time ◽  
Forest Canopy ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Satellite Systems ◽  
Position Accuracy ◽  
The Earth ◽  
Real Time Kinematic ◽  
The Future ◽  
Global Navigation Satellite

For more than two decade, the position on the earth can be precisely determined “real-time” in the order of few centimeters by Real Time Kinematic (RTK) GNSS (Global Navigation Satellite Systems) Method. Nevertheless, few limitations are still recognized such as degradation of accuracy against limited satellite visibilities (e.g. heavy satellite obstructions from forest canopy). It usually takes time to resolve the ambiguities or even in many occasion resulted in failure. Fortunately since recent years to the future seems more satellite systems beside GPS and GLONASS are being launched such as BEIDOU, GALILEO, QZSS, etc. It means that more satellite will be existed above the sky. The term GNSS has changed into Multi GNSS. This Multi GNSS is theoretically adding the value to previous GNSS System like GPS; problems of limited satellite visibilities (e.g. under forest canopy) to the position accuracy perhaps will reduce. Within this paper we try to do study the capabilities of RTK Multi GNSS under forest canopy in Indonesia. We observed by RTK in the forest areas which have canopy of 40 to 90 percent. As conclusion we found improvement in positioning result of even area of very limited satellite visibilities.

scholarly journals Research on Optimal Operation of Electricity Heat Hydrogen System Based on Evaluation of New Energy Consumption Potential

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinrui Liu ◽  
Xinying Zhao ◽  
Weiyang Zhong
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
System State ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy ◽  
New Energy ◽  
Different Levels

Under the background of the “double high” power system, the electricity heat hydrogen system (EHHS) plays a significant role in the process of energy decarbonization. In order to meet the different optimization objectives of the system under different new energy consumption states, a new energy consumption potential assessment and optimized operation method based on intuitionistic fuzzy rough set theory is proposed. By using the intuitionistic fuzzy rough set theory, the continuous attribute data is divided into different levels and the results of its membership and non-membership are gotten at different levels. The membership results of real-time consumption data are matched with the rule sets, and then the system consumption state judgment result is obtained. In this article, the system consumption situation is divided into five states, and compared with the traditional division method, so the system state can be described more comprehensively. At the same time, the fuzzy set is used to deal with the ambiguity of the boundary between each state. The intuition theory is used to solve the problem of the uncertainty of the consumption state, and then the accurate judgment can be realized. In response to different consumption states, an optimal scheduling model is established in which a hydrogen heat energy system (HHES) is involved to meet different requirements, and a hybrid particle swarm optimization algorithm is used to solve the model. Adopting the IEEE-30 bus system as the network structure of EHHS in the simulation, the analysis shows that the dynamic state division method based on intuitionistic fuzzy rough set theory can better be used to judge the system state according to real-time variable factors. The system optimization based on the consumption state division has the advantages of improving the operating economy and increasing the consumption of new energy.

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

scholarly journals A Kinematic Error Controller for Real-Time Kinematic Error Correction of Industrial Robots

2021 ◽  
Vol 53 ◽  
pp. 705-715
Author(s):  
Mitchell R. Woodside ◽  
Joseph Fischer ◽  
Patrick Bazzoli ◽  
Douglas A. Bristow ◽  
Robert G. Landers
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Industrial Robots ◽  
Kinematic Error ◽  
Real Time Kinematic

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

scholarly journals Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 657
Author(s):  
Aoki Takanose ◽  
Yoshiki Atsumi ◽  
Kanamu Takikawa ◽  
Junichi Meguro
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Estimation Method ◽  
Tall Buildings ◽  
Autonomous Driving ◽  
Reliability Estimation ◽  
Height Variation ◽  
Position Information ◽  
Real Time Kinematic ◽  
Evaluation Test

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness.

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