1. Why GNSS Resilience Matters
Global Navigation Satellite Systems (GNSS) underpin modern positioning, navigation, and timing (PNT), supporting critical infrastructure such as telecommunications, financial systems, power grids, as well as application domains including space and defence, emergency services, autonomous systems, and consumer and IoT devices.
Beyond positioning, GNSS provides the precise timing required to synchronise distributed systems, forming the foundation of positioning, navigation, and timing (PNT) applications. These applications depend on continuous, accurate timing—where even brief disruptions can lead to operational failures, safety risks, or significant economic impact.
However, GNSS signals are inherently fragile. By the time they reach Earth, they have weakened considerably and are easily affected by interference, jamming, spoofing, and environmental conditions. As a result, systems can no longer rely solely on constant signal availability.
This has led to the growing importance of ‘Assured PNT’ – the ability to maintain accurate and trustworthy positioning and timing even when GNSS signals are degraded or unavailable. In this context, it is no longer sufficient to consider performance under ideal conditions alone. Systems must also be designed to maintain accuracy when signals are weak, disrupted, or lost.
This distinction introduces a critical shift: from focusing purely on performance to designing for resilience—how well a system continues to operate when GNSS conditions deteriorate..
In GNSS-based systems, performance typically describes receiver accuracy under nominal and challenging operating conditions—such as low signal levels, multipath, or dynamic environments. Resilience, by contrast, refers to how well a system maintains positioning, navigation, and timing when GNSS signals are disrupted or unavailable.
High-performance oscillators underpin both performance and resilience: they improve signal tracking under weak conditions and preserve timing accuracy during signal loss. By maintaining stable time and frequency during signal disruptions, they enable systems to continue operating when GNSS is unavailable.
GNSS provides the external reference signals that enable positioning, navigation, and timing. A GNSS receiver processes these signals to calculate position and synchronise time.
At the core of this process is the oscillator, which provides the internal timing reference used for signal correlation, carrier tracking, and timekeeping.
Its role becomes increasingly critical as conditions degrade:
Under normal operation, oscillator performance affects signal tracking quality (e.g., via phase noise and C/N₀)
In degraded conditions, it impacts accuracy and robustness
During signal loss, it becomes the primary determinant of holdover performance and system resilience
As a result, oscillator stability directly links GNSS signal conditions to system-level PNT performance and reliability.
In practice, this means that the oscillator determines how quickly timing errors grow when signal quality degrades or is lost. This behaviour is governed by key characteristics such as short-term stability, long-term drift, and environmental robustness, which together define how well a system maintains accuracy during degraded conditions and holdover.
GNSS signals arrive at the receiver at extremely low power, typically around -130 dBm, with strong open-sky signals rarely exceeding -120 dBm.
At these levels, the signals are so weak that they are effectively buried in background noise within the receiver and cannot be directly observed. Instead, they must be recovered using correlation and signal-processing techniques that extract the signal from noise.
As a result, successful GNSS reception depends not just on absolute signal power, but on the carrier-to-noise density ratio (C/N₀), a measure of signal quality in GNSS receivers and how well a signal can be distinguished from background noise. Here, C represents the received carrier signal power, while N₀ represents the background noise power per hertz of bandwidth. Higher C/N₀ values indicate that the GNSS signal can be more easily detected, acquired, and tracked.
In practical terms:
Below ~ 25 dB/Hz: signals are weak and difficult to track reliably
30–40 dB/Hz: signals are usable
Above ~45 dB/Hz: signals are strong and provide high-quality tracking
A key factor influencing this balance is oscillator phase noise, which increases the effective noise within the receiver. As phase noise rises, C/N₀ decreases, making it harder to acquire and maintain signal lock—particularly in weak or interference-prone environments.
As a result, GNSS reception is inherently fragile and sensitive to real-world conditions:
Jamming: Strong interference overwhelms GNSS signals
Spoofing: Counterfeit signals mislead the receiver into calculating false position or time
Multipath: Reflected signals distort timing, especially in urban and maritime environments
Together, these factors highlight why GNSS systems must be designed for resilience, maintaining accuracy even when signals are weak, degraded, or unavailable, as summarised in Figure 1.
GNSS relies on precise correlation – aligning received signals with known satellite PRN (Pseudo-Random Noise) codes, and accurate timing; even minor disruptions to signal integrity or continuity have immediate consequences for PNT accuracy.
When GNSS is degraded or lost, systems must rely on internal timing references, primarily high-performance oscillators, to maintain accurate time and enable continued position estimation. The effectiveness of this transition depends critically on the quality of the oscillator.
At the core of oscillator performance is phase noise. Excess phase noise introduces short-term timing fluctuations, which translate directly into timing error. When GNSS is unavailable, these timing errors are no longer corrected by satellite references and begin to accumulate.
This accumulation defines holdover performance. Poor phase noise leads to faster growth of timing error, accelerating holdover degradation and reducing the duration over which the system can maintain acceptable accuracy without GNSS.
In many applications, GNSS does more than provide position—it serves as the primary timing reference that synchronises distributed systems such as telecommunications networks, power grids, and financial infrastructure. When GNSS is disrupted, this shared time reference disappears, and systems must rely entirely on their internal oscillator.
Without a highly stable oscillator:
Time error accumulates, degrading synchronisation in telecom networks, power-grid protection, and financial timestamping
Position uncertainty increases as timing error propagates into ranging errors
Systems enter degraded modes or fail, once timing and positioning exceed operational limits
In these scenarios, the difference between milliseconds and microseconds matters. GNSS resilience is therefore defined not only by signal reception, but by performance during signal loss, where oscillator phase noise directly governs timing stability, holdover capability, and ultimately overall system reliability.
No single parameter defines oscillator performance. Instead, resilience is determined by how multiple characteristics interact across different time scales and operating conditions. These characteristics influence system-level performance by affecting signal processing and timing stability when the GNSS receiver signals are degraded or unavailable.
Phase noise
Phase noise primarily affects signal acquisition and tracking performance by influencing the effective noise floor and C/N₀. Because GNSS signals are extremely weak, maintaining sufficient C/N₀ is critical for reliable reception. Increased phase noise raises the effective noise floor, making it more difficult to acquire and maintain signal lock, particularly in low-signal or interference-prone environments. While it is not a direct driver of holdover, it plays an important role in maintaining robust operation under degraded signal conditions.
Allan Deviation (ADEV)
ADEV characterises the evolution of frequency stability over time. It determines how quickly timing error accumulates during GNSS interruptions and is a key predictor of holdover performance, particularly over short to medium timescales.
Temperature stability
This parameter defines how well the oscillator maintains accuracy under changing environmental and dynamic conditions. This is especially critical in mobile, airborne, and space applications, where temperature variation and vibration can introduce additional frequency instability.
Ageing (Long-term predictability)
Ageing describes long-term frequency drift. Predictable ageing enables compensation and supports consistent performance over extended operational periods, improving long-term timing reliability.
Together, these characteristics define how effectively a GNSS receiver maintains performance across varying signal conditions, from normal operation to degraded environments..
While oscillator performance is a primary enabler of resilience, several system-level techniques further enhance robustness under challenging conditions. These include multi-constellation GNSS reception, sensor fusion with inertial systems, and interference detection and mitigation.
In addition, disciplined oscillator architectures, such as PPS Disciplined Oscillators (PPSDO) and GNSS Disciplined Oscillators (GNSSDO), combine high-performance oscillators with external timing references (e.g., 1PPS signals) to enable continuous calibration and improved long-term stability. By aligning the internal oscillator to an external reference during normal operation, these systems reduce accumulated timing error and improve readiness for GNSS outages, supporting extended holdover performance when signals are degraded or temporarily unavailable.
However, across all these approaches, a stable and accurate timing reference remains essential. Oscillator performance ultimately determines how well a system maintains accuracy during GNSS degradation and how long it can operate during signal loss.
When GNSS is degraded or lost, systems must rely on internal timing references, primarily high-performance oscillators, to maintain accurate time and enable continued position estimation. The effectiveness of this transition depends critically on the quality of the oscillator.
Once GNSS is unavailable, timing errors are no longer corrected by satellite references and begin to accumulate. The rate of this accumulation defines holdover performance.
Different oscillator characteristics dominate this behaviour over different time scales:
Short to medium-term stability/Allan Deviation (ADEV) determines how quickly timing error grows in the initial seconds to minutes of holdover
Temperature stability (FvT) influences frequency drift under changing environmental conditions
Ageing governs long-term frequency drift over extended outages
Together, these factors determine how long a system can maintain acceptable timing accuracy without GNSS and therefore define overall system resilience.
Table 1 illustrates how different oscillator performance characteristics influence holdover behaviour, highlighting how stability affects timing error growth during GNSS outages.
| Standard oscillators (TCXOs and OCXOs) | High-performance oscillators (Premium OCXOs, USOs) |
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Figure 2 illustrates how these differences translate into real-world behaviour, showing how timing errors accumulate over time for different oscillator performance levels.
High-performance oscillator solutions are therefore essential to maintaining system accuracy and reliability when GNSS outages occur, by minimising timing error growth and enabling extended holdover performance.
Achieving high-performance oscillator behaviour requires more than circuit design alone. It depends on precise control of materials, crystal behaviour, and high-resolution characterisation to ensure stable performance under real-world conditions.
Rakon’s approach focuses on detecting and minimising subtle frequency instabilities, such as micro-jumps and temperature-driven drift, while ensuring consistent performance from R&D to production, enabling reliable operation in demanding GNSS environments.
High-resolution temperature testing, combined with optimised crystal design, enables detection and reduction of short-duration frequency disturbances such as micro-jumps.
These small, rapid frequency variations can introduce timing errors that degrade signal tracking and positioning accuracy, particularly in weak-signal or high-dynamic environments.
These effects are often missed by standard testing but can introduce timing errors that impact receiver performance. Figure 3 shows how high-resolution testing reveals micro-jumps that would otherwise go unnoticed.
An oscillator’s frequency varies with temperature. This behaviour is characterised by the frequency slope (ΔF/ΔT), which defines how sensitive frequency is to temperature change.
In real-world environments, temperature is rarely constant. Even small fluctuations, caused by diurnal cycles, equipment heating, or platform motion, can introduce frequency drift. If the frequency slope is steep, these small temperature variations translate directly into frequency errors. By contrast, a low frequency slope minimises temperature-induced drift, improving stability, predictability, and holdover performance in dynamic conditions.
A lower and well-controlled frequency slope ensures that frequency changes with temperature are consistent and predictable, enabling more accurate compensation and stable performance in real-world conditions.
Typical frequency slope requirements for GNSS applications:
±5 to ±50 ppb/°C: Consumer grade GNSS applications
±0.5 to ±2 ppb/°C: High performance applications (Survey, RTK)
±0.1 to ±0.5 ppb/°C: Ultra high-end and infrastructure GNSS applications
Figure 4 illustrates a Rakon small-footprint OCXO achieving a frequency slope of ≤±0.15 ppb/°C across the operating temperature range of −40°C to +95°C, supporting demanding high-performance GNSS applications. This performance is enabled by Rakon’s advanced crystal resonator technology, ASIC design, and precision thermal & packaging control.
While frequency slope defines temperature sensitivity, real operating conditions often involve rapid temperature changes.
When temperature changes quickly, frequency drift can occur over short time periods. These fast changes are not always captured by low-resolution testing.
This is where high-resolution testing becomes critical—it provides enough measurement detail to detect short-duration and localised frequency variations.
Figure 5 demonstrates how test resolution affects the ability to detect frequency drift in real devices.
Together, these results show that both short-term disturbances (micro-jumps) and temperature-driven frequency drift directly affect oscillator stability. High-resolution characterisation is therefore essential to ensuring that device-level specifications translate into reliable system-level performance, ultimately supporting GNSS resilience.
The impact of oscillator performance varies across different GNSS and PNT applications. Table 2 summarises how specific challenges in key application areas translate into requirements for oscillator performance and stability.
| Applications | Challenge | value |
| Space and NewSpace PNT Systems |
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| Precision GNSS (RTK, Drones, Surveying) |
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| Emergency Beacons and Safety Systems |
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The evolution of assured PNT is expected to combine multiple approaches:
Multi-sensor fusion, integrating GNSS with inertial sensors and precision clocks
Complementary systems, such as LEO-based constellations, and emerging non-GNSS alternatives, providing more resilient and diverse PNT sources
Stricter resilience requirements, including interference detection and timing accuracy standards
Across all these trends, the need for high-performance oscillators remains unchanged. Regardless of how PNT architectures evolve, precision timing and frequency stability will continue to define system resilience.
GNSS resilience is no longer optional; it is a fundamental system requirement. While antennas and signal processing often receive the most attention, it is the oscillator—quietly maintaining time and frequency—that ultimately determines whether a system continues to operate when GNSS fails.
Because satellites transmit from orbit, their signals lose strength over distance and arrive at Earth near the noise floor (around −130 dBm). This makes them highly susceptible to interference and challenging to reliably detect and track.
Performance is accuracy when signals are available, while resilience is the ability to maintain accuracy when signals are degraded or unavailable.
Phase noise refers to short-term fluctuations in a signal’s phase.
In GNSS systems, even small increases in phase noise can reduce C/N₀ by several dB, weakening signal acquisition and tracking—especially in low-signal or interference-prone environments. By raising the effective noise floor, phase noise limits the receiver’s ability to reliably track weak signals.
Frequency drift over time is primarily characterised by oscillator ageing, which describes the gradual change in frequency due to internal material and structural effects. As frequency drifts, timing errors accumulate, reducing timing stability and impacting system performance, especially during GNSS outages.
Micro-jumps are extremely short, difficult-to-detect jumps in an oscillator’s frequency. Although subtle, they can introduce timing and positioning errors and increase the risk of GNSS signal loss. High-resolution testing is required to capture these rapid, transient frequency jumps, as they may be missed by standard measurement techniques.
Holdover is the ability to maintain timing using the internal oscillator when GNSS signals are lost.
In real-world conditions, where interruptions can occur, holdover performance determines how quickly timing errors grow. With a high-quality oscillator, errors can remain tightly controlled over minutes or longer, while lower-performance systems may drift to microseconds within seconds.