PNT Field Testing Moves Into the Lab

Positioning, Navigation, and Timing (PNT) technologies have become foundational to a wide range of modern applications, from consumer smartphones and wearable devices to connected vehicles, aviation systems, and critical infrastructure. At the heart of many PNT solutions are Global Navigation Satellite Systems (GNSS) such as GPS, Galileo, GLONASS, and BeiDou. As reliance on GNSS continues to grow, ensuring the accuracy, resilience, and reliability of PNT performance has become a critical requirement throughout the product development lifecycle. This eBook explores how a new approach to GNSS testing—signal decomposition—is transforming the way developers validate PNT systems by bringing the realism of field testing into the laboratory environment.

Traditional GNSS testing methods rely on a combination of simulation and field testing. Simulation provides repeatable, controlled, and highly configurable test environments, enabling developers to validate receiver performance throughout development. However, creating realistic scenarios can be time-consuming and often fails to capture the full complexity of real-world signal conditions. Conversely, GNSS record-and-playback solutions capture authentic signal environments, including interference, multipath effects, and environmental conditions, but offer limited visibility into the individual signal components and little ability to modify or analyze them. As a result, many organizations continue to depend on late-stage field verification testing, where issues are often more costly and time-consuming to identify and resolve.

The eBook introduces signal decomposition as a breakthrough technology that bridges the gap between simulation and field testing. Leveraging Spirent’s SimXTRACT software, recorded GNSS environments can be broken down into their individual signal components, transforming complex RF recordings into discrete, measurable, and editable signal paths. This process enables engineers to analyze key characteristics such as signal power, code error, Doppler error, angle of arrival, and multipath behavior. By extracting this level of detail from real-world recordings, developers gain unprecedented visibility into the signal environment and the ability to recreate highly realistic test scenarios within a simulator.

Beyond signal decomposition, the technology provides advanced analysis capabilities that help engineers identify and isolate specific conditions of interest within recorded data. Users can search for scenarios involving poor satellite visibility, low signal-to-noise ratios, multipath interference, spoofing events, jamming, or receiver anomalies. These conditions can then be converted into targeted simulation scenarios, enabling focused testing on critical events rather than replaying entire recordings. This dramatically improves testing efficiency while providing deeper insight into receiver performance under challenging operating conditions.

A key advantage of this approach is the ability to generate highly realistic simulation environments directly from field-collected data. Once decomposed, signals can be modified, enhanced, or combined with additional effects to create “what-if” scenarios that would be difficult, expensive, or impossible to reproduce in the real world. Engineers can adjust signal power levels, introduce interference, modify multipath characteristics, or evaluate performance under alternative constellation and frequency configurations. This flexibility allows organizations to validate receiver behavior across a broader range of operating conditions while maintaining the realism of authentic field data.

The eBook highlights how signal decomposition delivers value across multiple stages of the product development process. During receiver chipset design, developers can test positioning and measurement algorithms against realistic signal conditions much earlier in development, enabling faster optimization and validation of key capabilities such as acquisition, tracking, multipath mitigation, and spoofing detection. For receiver designers, signal decomposition enables more comprehensive performance characterization, accelerated troubleshooting, and the creation of realistic corner-case scenarios that improve overall test coverage. Integrators incorporating GNSS technology into vehicles, devices, and sensor fusion systems can gain earlier insight into real-world performance, helping identify issues before they become costly design challenges. The technology also enhances anechoic chamber testing by providing realistic signal arrival characteristics that significantly reduce setup times while improving test fidelity.

The operational and commercial benefits are equally significant. By enabling realistic GNSS testing from the earliest stages of development, organizations can shift testing activities left, reducing the cost and complexity of fixing issues later in the lifecycle. Development teams can shorten testing cycles, increase automation opportunities, improve root-cause analysis, and extract greater value from existing libraries of field recordings. The result is faster time to market, lower development costs, higher product quality, and greater confidence in PNT system performance.

Ultimately, this eBook positions signal decomposition as a major advancement in GNSS testing methodology. By combining the realism of field-recorded signals with the control, repeatability, and flexibility of simulation, organizations can achieve a more efficient and comprehensive approach to PNT validation. As GNSS-enabled technologies become increasingly critical across industries, signal decomposition provides a powerful framework for accelerating innovation, reducing risk, and delivering more robust and reliable positioning solutions.