Big-Data GNSS Integrity: What RIGOUR Means for Offshore Positioning QA

What RIGOUR Demonstrated

GMV NSL, the UK arm of the Spanish engineering group GMV, has completed the RIGOUR project (‘Real-time integrity for GNSS using opportunistic receivers’) with support from the European Space Agency under the NAVISP programme. The project demonstrates a departure from conventional GNSS integrity architectures: instead of relying solely on dedicated reference station networks to monitor satellite performance, RIGOUR aggregates measurements from thousands of everyday GNSS receivers – smartphones, vehicle navigation systems, mass-market positioning devices.

The team developed a simulation platform and GNSS integrity processor capable of handling measurements from 10,000 receivers operating across rural, urban, and dense-local environments. The system tested two distinct integrity services. The first, satellite (global) integrity, aggregates geographically distributed measurements to detect satellite faults and generate user differential range error (UDRE) values – the statistical parameters used to compute positioning protection levels. The second, local integrity, exploits dense clusters of GNSS users in urban environments to identify multipath, signal blockage, and interference effects. The system adjusts safety margins for affected areas without impacting users elsewhere.

Simulations under both nominal and degraded conditions showed the processor could detect moderate-to-large satellite anomalies. Detection performance improved when low-variance measurements from rural environments were included. The local integrity concept showed benefits in dense urban settings, though the team noted that severe non-line-of-sight signal conditions may require additional sensor-fusion approaches.

Why It Matters for Offshore Positioning

Offshore positioning quality assurance remains expensive and infrastructure-dependent. Current SBAS and RAIM integrity monitoring relies on shore-based networks or satellite-based augmentation services with limited coverage in many offshore regions. When you operate 200 nautical miles from the nearest reference station, you’re trusting integrity flags generated from data that may not reflect your local space segment geometry or signal environment.

RIGOUR demonstrates that statistical robustness – aggregating large volumes of noisy measurements – can compensate for individual receiver quality. This principle matters offshore because vessel-based GNSS receivers, particularly on smaller support vessels or ROVs, often exhibit higher noise than geodetic-grade reference stations. If crowdsourced integrity monitoring proves viable, it could provide real-time anomaly detection in regions where dedicated infrastructure is sparse or non-existent.

The project also highlights a conceptual shift in how we think about positioning QA. Traditional approaches treat integrity as a binary state: the system is either trusted or not. RIGOUR’s dual-service model – satellite-level integrity for space segment faults, local integrity for environmental effects – separates global from local error sources. That distinction aligns with how offshore positioning actually fails: satellite clock errors affect everyone, but multipath near a subsea structure affects only vessels nearby.

How the Principle Scales Down Offshore

Offshore environments differ fundamentally from the urban-rural scenarios RIGOUR simulated. Receiver density is orders of magnitude lower. A busy city block might have hundreds of GNSS-enabled devices per square kilometre. An offshore field has a handful of vessels across tens of square kilometres. The statistical aggregation that RIGOUR relies on – thousands of measurements per epoch – simply does not exist in most offshore contexts, and there is no realistic path to recreating it at sea.

This is an important caveat, not a footnote. RIGOUR’s headline mechanism – averaging out individual receiver noise across very large populations – largely fails to scale down to offshore receiver counts. What survives the transfer is something weaker and different: not big-data aggregation, but small-N cross-validation. With a handful of receivers you cannot drive down statistical variance the way RIGOUR does, but you can still cross-check independent receivers against each other to flag gross faults. The rest of this article treats the offshore case as that weaker mechanism, not as RIGOUR-at-sea.

Within those limits, the underlying idea remains relevant. Multi-vessel operations, particularly during subsea construction or well intervention campaigns, concentrate 5-15 GNSS receivers within a few kilometres. Platform clusters, especially in mature fields, create localised receiver networks. Autonomous surface vessels and survey USVs increase receiver density in survey corridors. These scenarios do not approach urban receiver counts and cannot deliver RIGOUR-scale statistical robustness, but they provide more distributed, mutually independent measurements than a traditional single-reference-station architecture – enough for cross-validation, not for population-scale averaging.

RIGOUR’s local integrity service – detecting environmental effects by exploiting dense clusters of users – has limited direct application offshore, and is best treated as not transferring. It depends on many receivers simultaneously experiencing the same multipath, blockage, or interference, which is exactly what an offshore field lacks. Multipath near subsea structures affects individual vessels, not dense user clusters. You won’t have 50 receivers simultaneously experiencing signal blockage from the same BOP stack.

What does apply offshore is not that service but a far simpler distributed cross-check between a few vessels. The actionable method is comparison, not clustering: if three vessels in close proximity all show sudden position jumps, that points to a space segment issue affecting everyone; if one vessel shows errors while nearby vessels remain stable, that points to a local problem on that vessel. This is a manual, small-N consistency check borrowed from the same intuition that motivates RIGOUR’s local service – not an implementation of it.

The satellite-level integrity service is where RIGOUR’s approach directly benefits offshore positioning. Detecting satellite clock errors, orbit faults, or signal anomalies through distributed measurements addresses a real gap. Current RAIM algorithms aboard vessels rely on internal consistency checks – they detect outliers within the observed satellite constellation but struggle with slowly evolving faults or satellite-specific biases. Crowdsourced integrity monitoring could identify these faults earlier by aggregating measurements across a wider geographic area.

Common Misconceptions in Offshore Integrity Monitoring

1. Treating GNSS Integrity as a Solved Problem

Many operators assume that because their GNSS receivers display integrity flags or RAIM warnings, they have adequate integrity monitoring. This assumption fails in remote offshore regions where SBAS coverage is marginal or non-existent. RAIM availability depends on satellite geometry and redundancy. Fault detection needs at least five satellites with adequate geometry, and fault detection and exclusion needs at least six, so with GPS alone RAIM availability degrades when visible satellites or geometry fall short. Multi-constellation GNSS improves availability, but does not eliminate fault detection latency or sensitivity limitations. Crowdsourced integrity monitoring could provide an independent check, particularly for detecting faults that RAIM struggles with – slow satellite clock drift, systematic biases, or constellation-wide timing errors.

2. Over-Reliance on Single-Source Integrity Flags

When a vessel’s GNSS receiver displays “integrity OK”, that flag typically reflects internal consistency checks – not independent validation against external references. The receiver has no way to detect a common-mode error affecting all satellites equally, such as a tropospheric anomaly or reference frame error. RIGOUR’s approach of aggregating measurements from multiple independent receivers provides redundancy at the integrity monitoring level, not just the positioning level. That’s the difference between detecting an outlier satellite (which RAIM does) and detecting a systematic fault affecting all satellites (which requires external validation).

3. Ignoring Statistical Robustness in Positioning QA

Traditional positioning QA focuses on measurement precision – reducing noise through filtering, averaging, or selecting high-quality satellites. RIGOUR demonstrates the opposite approach: accept noisy measurements but aggregate them statistically. This principle applies to offshore multi-vessel operations. If five vessels in a survey corridor independently compute similar satellite-specific residuals, that’s stronger evidence of a satellite fault than one high-precision reference station detecting the same anomaly. Offshore positioning specifications (IHO S-44 for survey accuracy, the IOGP/IMCA Guidelines for GNSS Positioning in the Oil and Gas Industry, IOGP 373-19 / IMCA S 015) emphasise accuracy and uncertainty quantification but give limited treatment to integrity monitoring redundancy.

4. Not Planning for Integrity Service Gaps

SBAS services like EGNOS or WAAS do not cover all offshore regions. Even where available, SBAS integrity messages may not update rapidly enough for dynamic positioning applications. Vessel operators often discover integrity service gaps only after mobilisation. RIGOUR’s crowdsourced approach, if implemented operationally, could fill these gaps – but only if sufficient receivers exist in the region. Mobilisation planning should include integrity service availability checks, not just positioning accuracy predictions. If operating in a region with sparse SBAS coverage, consider deploying additional GNSS receivers (on support vessels, subsea infrastructure, temporary shore stations) to create a local integrity monitoring network.

The Implementation Challenge Offshore

RIGOUR simulated 10,000 receivers. A typical offshore construction campaign involves 10-20 vessels. That three-order-of-magnitude gap is the central constraint, and there is no published figure – from RIGOUR or elsewhere – that tells you how few high-quality receivers can substitute for that many noisy ones. Statistical robustness improves with sample size, but the relationship is nonlinear, and the project did not quantify a minimum viable offshore receiver count. Anyone claiming a specific land-versus-sea trade is guessing.

What RIGOUR did find is narrower: including low-variance measurements from rural environments improved detection performance. Offshore conditions share that low-variance character – minimal multipath, clear sky visibility, stable receiver installations – so per-receiver measurement quality is typically higher than in the urban case. That higher quality mitigates the receiver deficit; it does not substitute for it. Better measurements help fault detection, but they cannot manufacture the population size that drives RIGOUR’s statistical robustness. A handful of excellent receivers is still a handful, and the gap from ten to ten thousand is not closed by quality alone.

The challenge is data aggregation infrastructure. RIGOUR’s processor requires real-time access to raw GNSS measurements from distributed receivers. Offshore vessels typically transmit processed positions via VHF or VSAT, not raw observations. Implementing crowdsourced integrity monitoring offshore would require standardised data formats, reliable communication links, and centralised processing. Existing vessel tracking systems (AIS, VSAT positioning services) already aggregate vessel GNSS data but typically at low update rates and without the raw observables needed for integrity monitoring.

That infrastructure exists in some forms. Oil-and-gas survey reference station networks already collect and distribute GNSS data. Differential correction services aggregate reference station measurements. VSAT-based precise positioning services (Fugro Marinestar, Veripos, etc.) operate global networks that could, in principle, support integrity monitoring functions. The missing piece is not technology but standardisation and operational implementation.

What This Means for Positioning QA Protocols

Current offshore positioning QA guidance (the IOGP/IMCA Guidelines for GNSS Positioning in the Oil and Gas Industry, IOGP 373-19 / IMCA S 015, alongside IMCA S 023 on the shared use of sensors) addresses accuracy, redundancy, and validation procedures but provides limited guidance on crowdsourced or distributed integrity monitoring. The assumption is that GNSS integrity is handled by the receiver’s internal algorithms or external SBAS services. RIGOUR suggests that positioning QA should include explicit integrity monitoring redundancy – multiple independent checks, not just multiple GNSS receivers feeding the same positioning filter.

Practically, this means:

• Deploy redundant GNSS receivers with independent processing. Not just dual receivers feeding the same POS/MRU system, but separate GNSS processors with independent integrity checks. Compare integrity flags between systems. If one system reports nominal performance while another flags a satellite anomaly, investigate before proceeding with critical operations.

• Use distributed measurements for cross-validation. In multi-vessel operations, compare satellite-specific residuals across vessels. Large discrepancies between vessels viewing the same satellite indicate a potential fault. This approach requires standardised data logging and post-processing but provides integrity validation impossible from single-vessel measurements.

• Plan for integrity service gaps in mobilisation. Verify SBAS coverage and integrity service availability before mobilisation. If operating in regions with limited coverage, consider deploying temporary reference stations or additional GNSS receivers on support vessels to create a local integrity monitoring network.

• Log raw GNSS observables, not just processed positions. Integrity monitoring requires access to range measurements, carrier phase, and satellite-specific residuals. Standard vessel positioning systems log processed positions but often discard raw observables. Retaining raw data enables post-mission integrity analysis and fault detection.

The Broader Industry Trend

RIGOUR is part of a wider shift toward opportunistic, distributed positioning architectures. Autonomous vessels, offshore wind operations, and subsea resident systems all increase the number of GNSS receivers in offshore environments. Each receiver is a potential contributor to integrity monitoring. The challenge is standardisation – ensuring data compatibility, quality, and access across operators and vessel types.

ESA and other space agencies are exploring crowdsourced GNSS for scientific applications (ionospheric monitoring, space weather) and safety-critical applications (aviation, maritime). Offshore operations sit between these extremes – less stringent than aviation but more demanding than most land-based applications. If crowdsourced integrity monitoring proves viable in aviation or urban autonomous vehicles, offshore adoption could follow.

The limitation remains sample size. Offshore receiver counts will never approach urban densities. But statistical robustness is not binary. Even modest aggregation – 20 receivers instead of one – improves fault detection and reduces false alarm rates. The question is whether operational benefits justify the infrastructure investment. For high-consequence operations (DP-supported well intervention, heavy lift, subsea tie-ins), the case is strong. For routine survey work with backup positioning systems, less so.

What to Watch

RIGOUR is a simulation-based demonstrator, not an operational service. The next step is field trials with real receivers in real environments. Watch for ESA or industry-led trials in offshore regions, particularly in areas with sparse SBAS coverage. If crowdsourced integrity monitoring proves viable, standardisation efforts will follow – data formats, communication protocols, quality control procedures.

Also watch for convergence with existing GNSS augmentation services. Commercial PPP and RTK correction services already operate global receiver networks. Adding integrity monitoring to these services is a logical extension. Some providers already build integrity, interference, and spoofing detection into their positioning services – Fugro Marinestar’s Satguard authentication and Veripos’s GRIT (GNSS Resilience and Integrity Technology) are current examples. Crowdsourced approaches could enhance these services or operate independently.

Finally, watch for regulatory developments. If aviation or maritime authorities recognise crowdsourced integrity monitoring as an acceptable alternative to traditional SBAS or RAIM, offshore adoption will accelerate. Until then, implementation remains voluntary and operator-specific.

RIGOUR demonstrates that large-scale aggregation of noisy measurements can provide robust integrity monitoring. Offshore environments differ from the land-based scenarios the project modelled, but the principles remain relevant. As receiver counts increase and data aggregation infrastructure matures, crowdsourced integrity monitoring could become a standard component of offshore positioning QA.

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Based on: GMV NSL Explores Big-Data Approaches for GNSS Integrity Monitoring