AI-Assisted PNT Anomaly Detection: Learning to See the Invisible

Part 2 of 3 of Bridge Connect Critical Infrastructure Resilience Series

“GNSS interference is no longer rare — it’s undetected. AI can change that.”

1 The Blind Spot

Positioning, Navigation and Timing (PNT) underpins everything from aircraft flight paths to 5G network synchronisation. Yet when GNSS signals degrade, most organisations realise it only after the damage is done — delayed ships, desynchronised networks, or corrupted timestamps.

The reason is simple: humans can’t see interference in real time. Traditional alarms in ECDIS, ADS-B, or telecom timing systems trigger only after accuracy thresholds fail.By then, the disruption has already propagated.

AI-assisted anomaly detection offers a new path: transforming raw signal data into predictive intelligence. It turns GNSS degradation from an invisible risk into a measurable operational variable.

2 Why Classical Detection Fails

2.1 Reactive, not predictive

Conventional GNSS monitoring depends on threshold alarms — e.g., C/N₀ < 25 dB-Hz or Position DOP > 6.

These indicate trouble only after interference takes hold.

2.2 Limited coverage

Ports, airports and telcos typically monitor a few receivers, not regional patterns. Each sees local noise but misses the spatial correlation that reveals large-scale spoofing or jamming.

2.3 Manual analysis bottleneck

Analysts must review thousands of logs from AIS, PTP, and NMEA sources. Human triage cannot keep up with the data volume or subtle temporal drift that signals early degradation.

Result: most interference remains undetected and unattributed — a perfect environment for malicious actors or cascading failures.

3 The Case for AI in PNT Monitoring

3.1 What AI brings

• Pattern recognition — learns baseline signal behaviour (carrier-to-noise, phase jitter, pseudorange residuals).

• Cross-sensor correlation — fuses data from GNSS, inertial, radar, telecom timing, AIS, ADS-B.

• Adaptive thresholds — dynamic alerting adjusted to location, time, and operational mode.

• Predictive modelling — forecasts degradation probability before accuracy loss occurs.

3.2 Core algorithms

AI Technique	Application	Outcome
Supervised learning (SVM, Random Forest)	Classify interference types (jam/spoof/multipath)	Fast, explainable alerts
Unsupervised anomaly detection (Isolation Forest, Autoencoders)	Detect new or evolving patterns	Continuous adaptation
Temporal models (LSTM, GRU)	Forecast signal drift	Predictive alerts
Reinforcement learning	Optimise receiver response or antenna beamforming	Self-tuning resilience

AI Model Pipeline for GNSS Anomaly Detection

4 Anatomy of an AI-Based PNT Observatory

4.1 Data inputs

1. GNSS observables — C/N₀, AGC, carrier phase residuals.

2. Auxiliary sensors — inertial data, magnetometers, radar.

3. Network data — PTP timing offsets, SyncE status, telecom logs.

4. External context — space-weather indices, NOTAMs, AIS and ADS-B feeds.

4.2 Data flow

1. Collection layer: Distributed sensors push real-time data to cloud or edge nodes.

2. Pre-processing: Normalisation, noise filtering, timestamp alignment.

3. Feature extraction: Derived metrics (e.g., phase-rate variance).

4. Model inference: AI models classify anomalies.

5. Alerting layer: Dashboards and APIs notify NOCs, VTS, or ATC systems.

4.3 Outputs

• Heat maps of jamming intensity (power vs time vs location).

• Spoofing probability scores (0–1 confidence).

• Forecast models — “likelihood of degradation in next 30 min”.

• Incident correlation with telecom timing and aviation data.

5 Global Examples of AI-Enabled GNSS Monitoring

United States – GPS Interference Detection & Mitigation (IDM) Programme

• DARPA and AFRL projects use machine-learning models on SDR data streams.

• Success: 95 % detection accuracy for low-power jammers at 20 km.

Europe – STRIKE3 and NAVISP AI Trials

• ESA/NAVISP pilots combine AI classifiers with crowdsourced receivers across 30 countries.

• Generates a continental “PNT Health Map.”

Japan / South Korea – Maritime AI Sensors

• AI fuses radar and GNSS discrepancies to flag spoofing near port approaches.

• Integrated into national VTS systems.

GCC – Emerging Concept

• Saudi CST and Qatari Ministry of Transport exploring PNT Anomaly Map prototypes.

• Opportunity: a regional “GCC PNT Observatory” using AI correlation across ports, airports, and telco POPs.

6 Building an AI-Driven Detection Capability

Stage	Action	Outcome
1 – Sensor Network	Deploy low-cost SDR or receiver probes at critical sites	Data visibility
2 – Data Lake & Fusion	Aggregate GNSS, PTP, AIS, and radar data	Shared situational awareness
3 – Model Training	Train AI on labelled interference events	Classification accuracy > 90 %
4 – Operational Integration	Connect alerts to NOC/VTS dashboards	Real-time mitigation
5 – Continuous Learning	Update models monthly with new data	Adaptation to evolving threats

7 Policy and Governance Dimensions

7.1 Data sharing

AI models need multi-source data. That requires:

• Legal frameworks for cross-agency exchange.

• Privacy controls and anonymisation.

• Cyber-security standards for PNT data lakes.

7.2 Standardisation

• ITU-R PNT Rec. M.2301 — outlines interference monitoring protocols.

• ENISA / ETSI developing AI explainability standards for critical systems.

• GCC regulators can harmonise around CST or TRA-UAE leadership.

7.3 Ethics & Explainability

Boards must ensure algorithms remain auditable and transparent. Black-box AI in safety systems is unacceptable; explainable-AI (XAI) frameworks should be mandatory.

8 Quantifying Value for Boards

Metric	Before AI	With AI Monitoring
Detection latency	10–30 min	< 30 s
False alarms	High (manual triage)	< 5 % with adaptive models
Geographic coverage	Fragmented	Regional, continuous
Operational continuity	Reactive	Predictive
Insurance risk premium	Baseline	−15 % typical reduction

The ROI lies not just in fewer disruptions, but in insurance, compliance, and investor confidence.

9 Integrating AI-PNT into National Resilience Architectures

1. Maritime: integrate anomaly alerts into VTS and ECDIS.

2. Aviation: feed AI-classified data into NOTAM and ATC systems.

3. Telecom: link timing-offset anomalies to network-synchronisation dashboards.

4. Energy: correlate PMU timestamp deviations with GNSS health data.

5. Defence: cross-feed interference intelligence into electronic-warfare situational awareness.

10 The GCC Opportunity — from Local Sensors to Regional Awareness

The Gulf has the ingredients to lead:

• Dense concentration of high-value PNT-dependent assets.

• 5G networks and cloud infrastructure suited for edge AI.

• Emerging terrestrial timing (eLORAN, fibre, R-Mode) providing complementary ground truth.

A GCC PNT Observatory, managed jointly by Saudi CST and partner regulators, could:

• Aggregate anonymised data from maritime, aviation, telecom, and energy sectors.

• Provide a common operating picture of interference events.

• Serve as a regional deterrent through transparency.

Bridge Connect recommends a public-private partnership model, aligning commercial telecoms with state regulators and research institutions.

11 Board-Level Checklist

Question	Board Action
Do we collect GNSS quality metrics continuously?	Mandate real-time monitoring.
Is AI integrated into our network-operations dashboards?	Include in 2026-27 investment plans.
Are our regulators or insurers requesting interference data?	Align reporting templates.
Do we share anomaly data with national authorities?	Establish data-sharing MoUs.
Have we modelled the cost of a 24-hour GNSS blackout?	Quantify and disclose in risk registers.

12 Bridge Connect Perspective

AI-assisted detection is not a luxury; it’s the next logical layer in digital sovereignty.Early adopters can position their telecom, port, or aviation systems as regional resilience benchmarks, attracting investment and policy trust.

Bridge Connect supports:

• Design of AI-PNT monitoring architectures

• Cross-sector data-fusion models

• Training for regulators and operators on AI governance in critical systems

Next in Series

Part 3 — Cyber-Resilience of Terrestrial Networks: Securing the New Ground Layer

We’ll examine how to defend the eLORAN, fibre, and microwave infrastructures that will soon carry the region’s sovereign time and navigation signals.