Image: Point One Navigation
IGNSS has transformed the way both individuals and machines navigate across the globe, leading to a growing number of organizations utilizing positioning data in the development of products and applications. GNSS technology plays a crucial role in enabling autonomous vehicles, robots, logistics fleets, and emergency response systems to accurately determine the precise locations of places, people, and things on Earth’s surface. As a result, routes are not only more accurate and efficient but also safer.
As a satellite-dependent navigation system, various atmospheric and technological factors can impact the accuracy and precision of GNSS signals. These signals often need to be corrected by receivers before they can be used for positioning, and various correction methods exist today to achieve this. Each method has its own advantages and disadvantages, catering to diverse accuracy requirements and application scenarios.
Five causes of GNSS signal inaccuracies
When choosing the best GNSS correction method for a specific project, it is important to comprehend signal errors and their underlying causes. GNSS errors result from a combination of elements, such as ephemeris inaccuracies, disparities in satellite clocks, conditions in the ionosphere and troposphere, and inconsistencies between various satellite systems. Each signal correction method addresses these elements differently, resulting in pros and cons that must be weighed before selecting and implementing a solution.
1. Inaccurate ephemeris data
To calculate their position on Earth, GNSS receivers need to know the exact position in space of the satellites they use. Satellite positioning and orbital parameters are represented in ephemeris data, but sometimes this data is incorrect. Ephemeris inaccuracies cause the receiver to not know the satellites’ exact positions, thereby degrading the accuracy of their position calculation.
2. Differences in satellite clocks
Even the highly accurate atomic clocks on GNSS satellites can introduce errors in the timestamps receivers use to calculate positions. The exceptionally high speed at which GNSS satellites travel in space (approximately 7,000 mph) adds another layer of complexity to these calculations because even a nanosecond of difference can lead to substantial positioning errors.
3. Conditions in the ionosphere
The ionosphere, the outermost layer of Earth’s atmosphere, consists of charged particles that can affect the speed of light and radio signals as they pass through it. Fluctuations in solar radiation and other ionospheric conditions can result in delays or distortions in GNSS signals, introducing measurement errors that require correction for precise positioning. Although the influence of the ionosphere can result in significant signal interpretation errors, correction methods can effectively model and account for them.
4. Conditions in the troposphere
Weather, which occurs in the troposphere, the innermost layer of Earth’s atmosphere, also impacts GNSS signals as they travel from satellites in space to receivers. Temperature, humidity, and pressure can affect the speed of light and radio signals much like the charged particles of the ionosphere, leading to even more delays and distortion in GNSS calculations. However, because weather is highly localized, tropospheric errors must be modeled and corrected from a relatively close distance to achieve the level of accuracy needed for precise positioning.
5. Group delay (code bias)
Different countries and organizations around the world operate GNSS satellites. While they are generally aligned, minor discrepancies in time references and frequencies exist that can impact the accuracy of GNSS positioning. This is known as group delay or code bias and must also be corrected to ensure that signals are interpreted correctly.
Types of GNSS corrections
Understanding the origin of errors is critical when selecting the optimal GNSS signal correction method for a particular product or application. Each method has advantages and disadvantages ranging in importance depending on the application of GNSS positioning.
Real-time kinematic positioning (RTK) correction is widely regarded as the best method for achieving precise GNSS signal correction. It requires setting up a base station with a GNSS receiver at a very well surveyed location near the target area (usually within 30-50 kilometers), which transmits corrections to the end user’s GNSS receiver (called the rover). The proximity between the base station and the rover mitigates the impacts of signal errors. Any signal disparities that do exist can be analyzed to measure positional differences between the base and the rover, enabling the latter to calculate its position very precisely.
Real-time kinematic positioning (RTK) yields efficient and highly precise GNSS corrections but requires an extensive network
of base stations to support receivers across a large geographic area. (Image: Point One Navigation)
However, classical RTK solutions have a notable limitation: to achieve corrections over wide areas they require an extensive infrastructure of base stations, which can significantly escalate costs.
Therefore, RTK is best for autonomous vehicles and consumer navigation and sub-optimal for positioning applications in remote areas.
Precise point positioning (PPP) utilizes a limited number of highly precise and accurate stations to correct GNSS signals. The PPP algorithm divides the responsibility for correction between these stations and GNSS receivers. As a first step, the PPP stations model various known sources of error within GNSS, such as ephemeris inaccuracies, clock discrepancies, and group delay. They then transmit this information to GNSS receivers to conduct further calculations based on local conditions and refine the error estimation. By combining the accumulated signal data with the known error sources provided by the PPP stations, GNSS receivers gauge both global and localized errors (including ionospheric and tropospheric effects), ultimately calculating the necessary signal corrections for accurate positioning.
Despite its high accuracy, the limited number of existing PPP stations results in a longer time for signal correction. Using the PPP method, signal correction may take approximately 20-25 minutes. Particularly challenging conditions can further prolong the time needed to correct the signal, as the receiver independently calculates both ionospheric and tropospheric effects.
PPP is best for heavy equipment operating in open water or remote locations and sub-optimal for consumer GNSS receivers and autonomous vehicles.
Precise point positioning (PPP) produces accurate signal corrections, but at a much slower speed than other solutions. (Image: Point One Navigation)
The forefront of GNSS signal correction technology today is state space representation (SSR). In addition to providing ephemeris, clock, and code bias discrepancy data like PPP, SSR offers valuable insights into other signal accuracy factors, even the highly localized interferences caused by the ionosphere and troposphere.
Nonetheless, many GNSS receivers lack the capability to effectively process and convert this extensive data into meaningful positions. To address this challenge, SSR data can be transformed into a virtual base station (VBS), effectively simulating an RTK base station for legacy receivers. This bleeding edge method enables the utilization of SSR data even with conventional GNSS receivers, expanding access to high-precision positioning capabilities to more users.
SSR is best for the automotive and robotics industries and sub-optimal for teams using generic receivers.
Choosing a GNSS correction method
Like all technology, GNSS correction methods are constantly evolving, making high-precision positioning more accessible and reliable across a wide range of applications. However, to serve the increasing demands of organizations using GNSS for applications requiring precise positioning, correction methods must be scalable, efficient and accurate.
Different methods for correcting GNSS signals offer varying levels of accuracy and suitability for specific applications. As they select which is best suited to their use case, users must prioritize their needs, as well as the benefits and trade-offs of each correction method. RTK produces fast, hyper-accurate results in developed areas but can be expensive to deploy in areas without the proper infrastructure. PPP methodology enables users in remote locations to access precise positioning information but can take a substantial amount of time. SSR is powering some of the most innovative applications in technology today, but is not as accessible as other methods due to the limitations of legacy receivers.
Once they have assessed cost, speed and accessibility, developers can select the GNSS correction method that is best for their product or application. As this continued innovation in the GNSS space increasingly helps organizations overcome challenges in signal correction, it will be interesting to see what new cutting-edge technology develops to shape the future of our world.