scholarly journals The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution

2013 ◽  
Vol 67 (2) ◽  
pp. 311-326 ◽  
Author(s):  
Paul D. Groves
Keyword(s):  
Intellectual Property ◽  
Development Effort ◽  
Positioning System ◽  
Modular Approach ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Diverse Range ◽  
Positioning Systems ◽  
User Communities ◽  
Complexity Problem

Navigation and positioning system users are demanding greater accuracy and reliability in ever more challenging environments. This is driving a wave of rapid innovation, with the result that multisensor integrated navigation systems will become much more complex. This introduces a number of problems, including how to find the necessary expertise to integrate a diverse range of technologies, how to combine technologies from different organisations that wish to protect their intellectual property, and how to incorporate new navigation technologies and methods without having to redesign the whole system. It also makes it desirable to share development effort over a range of different applications. To address this, the feasibility of a modular approach to the design and development of multisensor integrated navigation and positioning systems is analysed. Assessments of the requirements of different user communities and the adaptability of the different navigation and positioning technologies to different contexts and requirements are presented. Based on this, the adoption of an open interface standard for modular integration is recommended and the issues to be resolved in developing that standard are outlined.

scholarly journals Consistency of the Empirical Distributions of Navigation Positioning System Errors with Theoretical Distributions—Comparative Analysis of the DGPS and EGNOS Systems in the Years 2006 and 2014

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 31
Author(s):  
Mariusz Specht
Keyword(s):  
Normal Distribution ◽  
Root Mean Square ◽  
Statistical Tests ◽  
Position Error ◽  
Positioning System ◽  
Navigation Systems ◽  
Mean Square ◽  
Positioning Systems ◽  
Position Errors ◽  
System Errors

Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object’s position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900’000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.

An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

2010 ◽  
pp. 219-246 ◽  
Author(s):  
Denis Gingras
Keyword(s):  
Data Fusion ◽  
Mathematical Framework ◽  
Positioning System ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Future Evolution ◽  
Land Vehicle Navigation ◽  
One Step ◽  
Definition Of ◽  
Position Estimate

In this chapter, the authors will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, they will look at the mathematical framework and technologies involved to design positioning systems. The authors will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. They will then look at the case of robustness of the positioning system when one or some of the sensors are faulty and will describe how the positioning system can be made more robust and adaptive in order to take into account the occurrence of faulty or degraded sensors. Finally, they will go one step further and explore possible architectures for collaborative positioning systems, whereas many vehicles are interacting and exchanging data to improve their own position estimate. The chapter is concluded with some remarks on the future evolution of the field.

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

2014 ◽  
Vol 67 (6) ◽  
pp. 967-983 ◽  
Author(s):  
Zengke Li ◽  
Jian Wang ◽  
Binghao Li ◽  
Jingxiang Gao ◽  
Xinglong Tan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Integrated System ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Positioning Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Fuzzy Neural

The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

Integration of Satellite and Inertial Positioning Systems

1990 ◽  
Vol 43 (1) ◽  
pp. 48-57 ◽  
Author(s):  
M. Napier
Keyword(s):  
Global Positioning System ◽  
System Integration ◽  
System Performance ◽  
Inertial Navigation ◽  
Positioning System ◽  
Navigation Systems ◽  
Relative Positioning ◽  
Inertial Navigation Systems ◽  
Positioning Systems ◽  
Global Positioning

The Global Positioning System (GPS) offers an absolute positioning accuracy of 15 to 100 metres. Inertial navigation complements GPS in that it provides relative positioning and is totally self-contained. These two positioning sensors are ideally suited for system integration for although there is not necessarily an improvement in accuracy, the integration of GPS with inertial navigation systems (INS) does enable an increase in system performance.

Thruster Design for Acoustic Positioning Systems

1975 ◽  
Vol 12 (02) ◽  
pp. 122-137
Author(s):  
Neal A. Brown ◽  
John A. Norton
Keyword(s):  
Positioning System ◽  
Navigation Systems ◽  
Noise Levels ◽  
Cavitation Noise ◽  
Positioning Systems ◽  
Ocean Drilling ◽  
Cavitation Performance ◽  
Propeller Blades ◽  
Large Improvement ◽  
Acoustic Positioning

The cavitation noise of ducted transverse thrusters threatens to interfere with the operation of acoustic positioning or navigation systems as employed on ocean drilling ships, mining ships, and other work or scientific vessels so equipped. The design of an acoustically and hydrodynamically sophisticated transverse thruster is described in terms of its gross parameters and reasons for their selection. The design of the CRP propeller blades, which are of highly unorthodox form, is discussed in detail along with its conceptual basis. Model tests show that a large improvement in cavitation performance has been achieved while also maintaining efficiency. Measurements of the acoustic baffle effectiveness of the thruster duct, and engineering means to realize it, are described and applied to estimation of the interference noise levels expected to be sensed through the vessel's positioning system hydrophones.

Multisensor Data Fusion Applied to Marine Integrated Navigation Systems

2005 ◽  
Vol 219 (3) ◽  
pp. 121-130
Author(s):  
A Tiano ◽  
A Zirilli ◽  
M Cuneo ◽  
S Pagnan
Keyword(s):  
Data Fusion ◽  
Theoretical Background ◽  
Accurate Estimation ◽  
Positioning System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
State Variables ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Multisensor Data

This paper deals with the problem of designing a flexible and accurate integrated navigation system (INS) for marine craft. The proposed INS is based on the integration of a global positioning system (GPS) with a compass and a speed log. After introducing the scopes and functions of the proposed INS, mathematical models of its main components are presented. Then the development of a new multisensor data fusion algorithm for carrying out an accurate estimation of the main state variables is presented. The theoretical background for the sensor fusion is based on the classical Kalman filter theory, which allows to update an a priori position estimate, given by a dead-reckoning system, with the information supplied by a GPS positioning system. Finally the filtering algorithm is extended in the framework of interval analysis and fuzzy set theory in order to improve the reliability and robustness of the estimation algorithm. The validity of the proposed approach is demonstrated by simulation examples applied to a container ship navigating in realistic conditions.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

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