PD CEN/TR 17447:2020
Space. Use of GNSS-based positioning for road Intelligent Transport System (ITS). Mathematical PVT error model
Standard number: | PD CEN/TR 17447:2020 |
Pages: | 62 |
Released: | 2020-02-12 |
ISBN: | 978 0 539 06293 9 |
Status: | Standard |
PD CEN/TR 17447:2020 - Space. Use of GNSS-based Positioning for Road Intelligent Transport System (ITS). Mathematical PVT Error Model
Welcome to the future of road transportation with the PD CEN/TR 17447:2020 standard, a comprehensive guide that delves into the use of GNSS-based positioning for road Intelligent Transport Systems (ITS). This standard is a crucial resource for professionals and organizations involved in the development and implementation of advanced transportation technologies.
Overview
The PD CEN/TR 17447:2020 standard provides a detailed mathematical model for Position, Velocity, and Time (PVT) error analysis in GNSS-based positioning systems. Released on February 12, 2020, this document is an essential tool for understanding and mitigating errors in GNSS data, which is critical for the accuracy and reliability of ITS applications.
Key Features
- Standard Number: PD CEN/TR 17447:2020
- Pages: 62
- ISBN: 978 0 539 06293 9
- Status: Standard
Why Choose PD CEN/TR 17447:2020?
As the world moves towards smarter and more efficient transportation systems, the role of GNSS-based positioning becomes increasingly significant. The PD CEN/TR 17447:2020 standard is designed to address the challenges associated with GNSS data accuracy, providing a robust framework for error modeling. This ensures that ITS applications can operate with the highest level of precision, enhancing safety, efficiency, and user satisfaction.
Comprehensive Error Modeling
The standard offers a thorough mathematical model for PVT error analysis, which is crucial for developers and engineers working on ITS projects. By understanding the potential sources of error in GNSS data, professionals can design systems that are more resilient and reliable, ultimately leading to better performance and user experience.
Enhancing ITS Applications
With the insights provided by the PD CEN/TR 17447:2020 standard, ITS applications can achieve unprecedented levels of accuracy. This is particularly important for applications such as autonomous vehicles, traffic management systems, and navigation services, where precision is paramount.
Future-Proofing Transportation Systems
As transportation systems continue to evolve, the need for accurate and reliable positioning data will only grow. By adopting the guidelines and models outlined in this standard, organizations can future-proof their ITS solutions, ensuring they remain at the forefront of technological advancements.
Who Should Use This Standard?
The PD CEN/TR 17447:2020 standard is ideal for a wide range of professionals and organizations, including:
- ITS developers and engineers
- Transportation planners and policymakers
- GNSS technology providers
- Academic and research institutions
Conclusion
In a world where precision and reliability are key to the success of Intelligent Transport Systems, the PD CEN/TR 17447:2020 standard stands out as an invaluable resource. By providing a detailed mathematical model for PVT error analysis, this standard empowers professionals to create more accurate and efficient ITS solutions, paving the way for a smarter and safer transportation future.
Embrace the future of transportation with the PD CEN/TR 17447:2020 standard and ensure your ITS applications are built on a foundation of accuracy and reliability.
PD CEN/TR 17447:2020
This standard PD CEN/TR 17447:2020 Space. Use of GNSS-based positioning for road Intelligent Transport System (ITS). Mathematical PVT error model is classified in these ICS categories:
- 03.220.20 Road transport
- 33.060.30 Radio relay and fixed satellite communications systems
- 35.240.60 IT applications in transport
This document is written in the frame of WP1.3 of GP-START project. It discusses several models to provide synthetic data for PVT tracks and the ways to analyse and compare the tracks to ensure these are similar to the reality.