New 3D mapping techniques are a game-changer for global positioning

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For most of human history, the ability to pinpoint your location would have been nothing short of magical. Nowadays, thanks to GNSS (Global Navigation Satellite System) technology, it barely raises an eyebrow. Yet, as ubiquitous as GNSS has become in commercial, civilian, and military applications, it’s still not a perfect solution. The biggest problem: the lack of reliability.

As long as the receivers have a clear line of sight to the GNSS satellites, positioning works just fine. And most of the time they do, especially in the air, at sea or on the highways. Unfortunately, in many places, buildings and terrain can interfere with satellite signals, causing significant errors and even loss of focus. And positioning systems generally have no way of predicting when and where these large errors will occur.

Technologists have long considered this an inherent, albeit regrettable, feature of GNSS. After all, we can’t change the laws of physics to make satellite signals immune to reflection or interference. With new 3D mapping techniques, however, we can do something just as useful: understand exactly where and when GNSS can be reliable, and where it can’t. Today, industry leaders are using these techniques to radically improve the positioning of current and emerging use cases. And they are rewriting the rules of what is possible with GNSS.

Navigating GNSS Challenges

The strength of GNSS technology lies in its use of satellite constellations. Receivers only need a clear line of sight to a few of them to accurately establish position, and most of the time they can get it, even when most satellite signals are blocked . The problem: GNSS satellites are constantly moving, orbiting the Earth at about one degree per minute.

If you’re calculating position in an urban area with tall buildings, you can usually trust the GNSS signals, but there’s no way of knowing when you shouldn’t. Reflections on buildings in particular can disturb the positioning of several tens of meters (Figure 1). It’s not like positioning systems can just avoid bad spots either, because they change as the satellites move, literally second by second.

Figure 1. GNSS signal interference

Until now, this problem has not been a decisive factor for most commercial GNSS applications. As we move towards more ambitious use cases, such as autonomous vehicles or drone delivery, reliable positioning data becomes absolutely essential.

Changing the Game with 3D Mapping

Today, industry leaders are applying new techniques to solve this problem. By combining GNSS data with high-resolution 3D maps, they can identify exactly where GNSS signals will be available and trustworthy, and where they may not, second by second. And they can offer this information (usually as a cloud service) to any commercial customer or government agency that could benefit from more reliable positioning.

Here’s how the technology works: Companies that want to provide GNSS reliability services start with a fine-grained 3D map of a given area. For aviation and automotive use cases, organizations are using mapping solutions that offer native resolutions down to 30 centimeters, with the ability to derive even higher definition resolutions. They also use techniques such as multispectral imaging to differentiate between natural and man-made materials, penetrating through smoke, and more.

Once these companies have a high-fidelity map, they create an overlay of observable points in that area, such as dividing the region into one-meter squares. Then, from each of these points, they determine which satellites will be clearly visible at any given time. There are several ways to make this determination. The most popular include:

  • Shadow Match: This technique, often applied to smartphones, creates a 3D map-assisted (3DMA) grid showing predicted GNSS signals for a given area. It then compares these predictions to measured signals in real time to identify and correct errors.
  • Laser trace: In other places, especially aviation, where more granular elevation and azimuth detail is needed, the primary solutions use ray tracing from every observable point to every satellite in a constellation. They do this repeatedly, creating a dataset that shows which satellites are in line-of-sight (and therefore reliable) second by second, on every meter of a 3D map (Figure 2).
Spirent Ray Tracing 3D Mapping
Figure 2. Identification of GNSS reliability via ray tracing

Next-gen positioning in action

GNSS reliability solutions apply different techniques for different use cases. In addition to how a system calculates line of sight, the most important factor that dictates the approach to a solution is the speed of the calculation and the number of points. There are two main options:

  • Calculation of the device for a single point: For applications with large local computing resources, where some latency in positioning computation is acceptable, and where the device only wants to know one (or a few) points, a device-centric solution is appropriate. This approach is used in most smartphone-based scenarios, where applications have a strong endpoint device, typically a strong network connection, and do not require precision measured in milliseconds.
  • Scalable cloud-based solution: There are, however, many scenarios, such as aerial drones and driverless vehicles, where the entire environment around the vehicle or route is needed quickly and the vehicle does not have dedicated GNSS computing. . In these cases, GNSS cloud services can offer real-time predictive information. They do all the calculations for an area in advance in the cloud, predicting a certain period in the future. Positioning engines can then understand in advance and in real time exactly where they can trust GNSS signals and where they cannot, and proactively mitigate issues. Or better yet, use predictions to extract the best possible signals and improve performance.

Look forward

Implemented effectively, early integrations have shown that these techniques can literally double the accuracy of GNSS systems. But the potential goes much further in multiple aeronautical, automotive and military scenarios:

  • Aviation: 3D mapping techniques enable smarter flight planning, allowing organizations to know in advance where and when it is safe to fly, and adjust flight times to optimize positioning reliability. Or, in large-scale drone applications like drone delivery, companies can choose routes that are known to be reliable at specific times.
  • Automotive: Automakers can use real-time predictions to improve GNSS system performance and improve safety by knowing where and when GNSS is reliable as part of all sensors in the car.
  • Military: Mission planners can improve risk analysis by understanding best and worst case scenarios for GNSS performance in a given area. They can also assess the integrity of GNSS signals in the field, including detecting blocking or spoofing of positioning data.

These are just a few of the possibilities. Thanks to new 3D mapping techniques, we can transform GNSS from “generally reliable” to very accurate GNSS. And we can begin to harness the potential of positioning for applications we are only just beginning to imagine.

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