Recreating Reality in Lab-Based GNSS Testing

Practice makes perfect. But how you practice goes a long way towards deciding just how close to perfection you can get. This is particularly true when developing products that depend on precision technologies like Global Navigation Satellite Systems (GNSS).

New applications in areas like autonomous vehicles, aerospace, and precision agriculture demand ever-greater positioning accuracy, in ever-more-challenging scenarios. In response, system integrators and original equipment manufacturers (OEMs) are rethinking every aspect of GNSS engineering - including how they put products through their paces during development. Many have reached the same conclusion: the more realistic they can make GNSS lab testing, the higher performance they can achieve, and the faster and more efficiently they can get products into the hands of users.

What does realism actually mean in GNSS simulation? And how can OEMs achieve the most lifelike test environments possible? Let’s take a closer look.

Replicating Reality in the Lab

It’s not hard to imagine the most realistic test environment possible: just take your device outside and see how it works. Unfortunately, the real world is less-than-ideal for testing, as there are just too many unknowns to control for. If a prototype drone loses reception during an outdoor test flight, for example, how can you tell what caused that signal loss? Was it radio frequency (RF) interference? Blockage from a nearby structure? Poor antenna placement? Without knowing what interference sources are in the environment, or what satellites are (or should be) in view, there is little-to-no traceability on error sources. Have you improved the performance of your device? Or were the test conditions simply more favorable?

Real-world testing absolutely has a place in the verification stage of product development. But it’s controlled lab testing that lets you quickly zero in on performance issues and their causes—ideally, earlier in the development cycle, when they’re less expensive to fix. A high-performance testing environment should be capable of replicating as closely as possible the conditions the Device Under Test (DUT) will encounter in the real world. That’s easier said than done. Even the most sophisticated digital twin, for example, can’t replicate every inch of thousands of miles of signal propagation or every transient noise signature seen in real-world environments.

If you can’t fully represent reality, however, you can get closer by incorporating more accurate models into GNSS lab simulation. That includes modeling GNSS satellites and their transmissions, GNSS receivers, the physical environment through which signals propagate, and the structure and dynamics of the product (say, a vehicle or drone) in which the device will operate. Ultimately, you should strive to:

• Faithfully replicate GNSS signals in a way that can be controlled, repeated, and modified
• Use systems that can incorporate more accurate external data and models over time
• Use simulation equipment that’s more accurate than the DUT—by an order of magnitude—so you can verify that errors or anomalies observed are actually produced by the device, not the test equipment

Key Considerations for Realistic Testing

To make lab simulations as realistic as possible, focus on the following factors:

Signal modeling: GNSS systems have Interface Control Documents (ICDs), which describe how satellite signals should be seen by the receiver, taking into account factors like atmospheric interference, clock bias, and ephemeris errors. A good simulator should accurately implement ICD parameters and support ongoing upgrades to remain aligned with the ICD as it changes.

Space weather and atmospheric effects: ICDs provide mathematical models for simulating space weather and atmospheric interference, but these models can never be 100% accurate, as real space weather constantly changes in unpredictable ways. You typically can’t field test for such effects either.  The science of scintillation, for example, is not precise enough to pin down a specific time and location to test against it. Nor would such tests be repeatable. In some cases, such as when designing spacecraft navigation systems, field testing is typically not possible at all. Good simulations can, however, use high-resolution models of certain effects to enable testing that’s almost as useful. For example, you can model solar activity to see how it impacts spacecraft navigation systems, or measure the effects of ionospheric scintillation on precision agriculture systems. 

Vehicle modeling: The more realistically a simulator represents vehicle characteristics and dynamics, the more realistic the test results. As well as modeling structural impediments to the antenna, they should also be able to simulate realistic motion and trajectory, so you can observe how the receiver performs when the vehicle is rapidly accelerating or changing direction. Note that for highly dynamic scenarios, the faster the simulator’s update rate, the more accurately it can plot the vehicle’s attitude and position. Further, with the inclusion of inertial sensors in most modern vehicle positioning engines, a simulator’s ability to emulate realistic sensor outputs and errors enables a more representative picture of real-world operation to be seen in the lab.

Local environment: One way in which realism makes a huge difference is in simulating local environments. When you can model things like multipath signals and blockages in specific environments using real 3D maps (for example, modeling the effects of skyscrapers in downtown Chicago on vehicle navigation systems), you can better understand how the device will handle more challenging real-world scenarios.

RF record and playback: Using RF recordings from real-world locations and routes can enable highly realistic testing - especially useful for devices that will operate in a specific environment, such as mines or container ports. For maximum realism, look for equipment that features:

1. High-resolution signal recording, with higher bit depth than the DUT
2. High dynamic range to pick up weak signals in noisier environments
3. High-quality build and design, so the equipment doesn’t introduce noise from its own internal components

Optimizing Simulations

As more demanding applications rely on GNSS, more of the industry is recognizing the importance of realistic lab testing. Indeed, in many cases, improving the accuracy of simulations can be the most important step an OEM takes to reduce development costs and timelines. 

Fortunately, new tools continue to emerge that enable higher-fidelity testing. For example, it’s now possible to combine 3-D mapping with ray-tracing to simulate signal environments. By precisely modeling line-of-sight to GNSS satellites, as well as the buildings and features that obscure those lines of sight, you can measure exactly how your device will handle real-world multipath signatures and degraded satellite availability – even for specific operating locations.

With capabilities like these, your lab environment gets ever-closer to mirroring the real world, without sacrificing repeatability or control. So, even if you can’t recreate the real world in your testing environment, you’ll be able to deliver products with exceptional accuracy faster and deliver them to customers with greater confidence.