Global Navigation Satellite Systems Engineering, Policy, and Design
RTCA is working to develop new standards to tackle GNSS growth and challenges. Here’s an update on the special committee’s progress, provided during a recent webinar.
By Dawn M.K. Zoldi (Colonel, USAF, Ret.)
Errors in inertial sensor measurements are accumulated over time, leading to drift in INS navigation outputs. While gyro bias is the main contributor, other factors, such as accelerometer bias and noise, are important to consider for balancing the overall error budget.
By Andrey Soloviev
Robustness to GPS jamming and spoofing is critical for military applications yet challenging, and numerous alternative sensing techniques have been explored over the years. Virtual aiding methods have proven effective, with the ability to constrain and localize the search space for improved operability in GPS-denied environments.
By Inside GNSS
A: GNSS users who must verify the integrity of their navigation solutions to meet safety requirements need at least some knowledge of the degree to which they can trust the signals received from GNSS satellites before additional monitoring is added.
By Inside GNSS
The SAGAIE network was deployed in West-Africa in 2013 to assess the feasibility of an equatorial for ASECNA SBAS by studying ionospheric scintillation. This study of the network’s measurements, taken between 2013 and 2016, analyzes the characteristics of the scintillation recorded by five receivers that cover Sub-Saharan West-Africa.
By Inside GNSS
Speakers at the 14th European Space Conference in Brussels discussed the need to defend EU assets against unfriendly attacks. Recent events in Eastern Europe would seem to lend force to such concerns, as the European Union makes headway toward the intersection of civil infrastructure and security and defense.
By Peter Gutierrez
Will the network, which some are still fighting, be ready for a September rollout, and if so, what are the implications for GPS and the people who rely on it?
By Dawn M.K. Zoldi (Colonel, USAF, Ret.)
A factor-graph optimization-based GNSS positioning method uses GNSS pseudorange and Doppler observations to estimate position, velocity, and receiver clock biases. Added constraints on past and current graph nodes of the graph using time-difference observations of the GNSS carrier phase improve the accuracy, and a robust optimization method excludes multipath outliers. Experimental results reduce horizontal positioning error from 5 to 10 meters to 1.37 meters.
By Inside GNSS
This research derives effective formulas relating GNSS meta-signal observations to the measurements obtained from its side-band components, when processed independently in a standard receiver architecture. Meta-signal processing can lead to high-accuracy solutions even under harsh conditions.
By Inside GNSS
In this second column we consider fundamental principles of inertial navigation that derive position, velocity and angular orientation from measurements of accelerometers and gyroscopes.
By Andrey Soloviev
The European Geostationary Navigation Overlay Service (EGNOS) will soon launch a new maritime solution that will make legacy and costly coastal ground-based augmentation systems redundant. Meanwhile, the next generation EGNOS V3, featuring dual-frequency, multi-constellation (DFMC) services, is set to come online by 2028, once GPS L5 is declared operational.
By Peter Gutierrez
A novel method for improving the positioning accuracy of GNSS receivers exploits a machine learning (ML) algorithm. The ML model uses the post-fit residuals, which are readily available after the position computation from the position, velocity and timing (PVT) engine, adoptable by existing receivers without requiring any modification. The performance of this method, demonstrated using data collected with mass-market receivers as well as a Google public dataset collected with Android smartphones, shows the practicality of the concept.
By Inside GNSS
The concept of autonomous driving has generated a lot of interest and attention in the past decades as it is believed to provide numerous benefits for individuals and society: increased road safety, reduced traffic congestion, accidents and death, and saving time and pollution on commuting. There is a lot of ongoing work on this topic, much research and many experiments have been conducted on how to make cars learn the environment, make human-like decisions and drive on their own.
By Inside GNSS
