Data quality standards

🧪 1. Validator Frameworks

Validators are software modules or nodes that monitor, verify, and rate incoming GNSS data.

Onocoy’s validators check:

📍 Position consistency (is the station where it claims to be?)
📈 Measurement integrity using multiple GNSS techniques:
- SPP (Single Point Positioning)
- DGNSS
- RTK
- PPP-AR (Precise Point Positioning with Ambiguity Resolution)
🛰 Satellite signal behavior (tracking stability, constellation coverage)
🌐 Network comparison with other nearby stations

🛑 2. Spoofing and Fraud Detection

Mechanisms are in place to detect:

🎭 Spoofed signals: Signals that are faked to simulate GNSS reception.
🤖 Synthetic data: Correction data that mimics authentic signals but isn't backed by real observations.

Detection techniques include:

Statistical outlier detection
Physical plausibility checks
Comparison against trusted reference stations
Network-wide bias analysis

📡 3. Real-Time Signal Quality Metrics

Stations are continuously monitored for:

Metric

Description

SNR

Signal-to-noise ratio — higher = better

Cycle Slips

Loss of phase lock — fewer = more reliable

Multipath

Reflected signals — lower = better

GDOP

Satellite geometry — lower = better precision

Ambiguity resolution rate

Essential for RTK performance

These metrics contribute to a quality score assigned to each station in real time.

📊 4. Automated Scoring & Quality Ranking

Each station is scored based on:

Supported signals (GPS, Galileo, BeiDou, etc.)
Frequency bands (L1, L2, L5, etc.)
Data completeness (uptime and continuity)
Data accuracy (post-fit residuals, position error)

Stations that meet or exceed quality thresholds:

✅ Get full rewards (base + usage incentives)
⚠️ Underperforming ones are flagged or demoted
❌ Suspicious stations can be excluded or blacklisted

💰 5. Incentive Mechanisms

In platforms like Onocoy, rewards are tied directly to data quality:

🎯 Base Reward: Scaled by signal diversity, availability, and quality
🔄 Usage Reward: Based on how often the data is used by customers
🧭 Location Bonus: Encourages quality deployment in under-served areas
⚠️ Penalty Zones: Overlapping stations may face reduced rewards

👉 This creates a self-regulating ecosystem where participants are financially motivated to ensure data quality.

🛠 6. Open Standards and Interoperability

Using open GNSS data standards like:

RTCM 3.x + MSM (for real-time corrections)
NTRIP (for data transmission)
RINEX (for raw data archival and post-processing)

Ensures:

Broad hardware compatibility
Transparent data formats
Easier quality validation by external tools

🧠 7. Cross-Validation and Network Effects

In a network like Onocoy:

Stations are compared with nearby peers
Network-level solutions verify consistency
Long baselines are analyzed for atmospheric delay mismatches

➡ This makes it extremely hard to cheat or fake data without being detected.

✅ TL;DR: Key Mechanisms to Ensure GNSS Data Quality