Understanding the risks to GNSS-based tracking:
Modern tracking systems depend on GNSS. That makes them vulnerable: spoofing can deceive receivers into reporting false position/time, and jamming can disrupt reception and cause data gaps or loss. GeoTRK is designed to protect both integrity (is the location real?) and confidentiality (who can see it, and how is it protected?).
Jamming
interference that overpowers signals and can cause partial/complete loss.
Spoofing
convincing fake signals that can produce incorrect data for longer periods, sometimes without obvious loss of signal power.
Research surveys group GNSS spoofing/jamming detection into multiple categories—signal quality monitoring, power-based checks (e.g., C/N0, AGC), correlation-peak monitoring, timing/angle methods, NMEA message analysis, pseudorange integrity checks, and machine/deep learning. GeoTRK combines these ideas into a practical, production-minded pipeline:
Layer 1 — Real-time GNSS Health Checks
We continuously evaluate receiver/measurement signals commonly used to detect interference:
- Signal power / C/N0 trends: useful, but not sufficient alone for sophisticated spoofing
- AGC behavior: simple to integrate; stronger when combined with other signals
- Correlation-peak monitoring: targets abnormal correlation behavior
Layer 2 — Message & Measurement Consistency
We cross-check internal GNSS consistency using approaches such as:
- NMEA sentence integrity/consistency analysis
- Pseudorange integrity-style checks (a major class of methods used to spot spoofed positioning)
Layer 3 — Machine Learning Classification
Reviews emphasize ML/DL as a major trend for detecting and classifying spoofing/jamming, often using public datasets (e.g., TEXBAT, OAKBAT). We use ML where it adds signal—primarily for pattern recognition across multiple indicators rather than single-metric alerting.
Layer 4 — Network-Aware Monitoring and Escalation
Some approaches detect spoofing across an area using network monitoring concepts (detecting patterns that indicate a common spoofer). GeoTRK is designed to support fleet-wide correlation—so one suspicious event can harden the system for all assets in the same region/time window.
We build autonomous detection as a reasoning + acting loop: the system forms a hypothesis, runs verification steps, and logs what it observed and why it concluded an incident occurred. This mirrors the core idea behind ReAct-style systems: interleaving reasoning with actions to improve robustness and allow inspection/correction by humans.
Example "Reason → Act → Observe" Flow (Simplified)
Observe: sudden location jump + unusual GNSS health metrics
Reason: "Could be spoofing, jamming, multipath, or device reset"
Act: run additional checks (signal-quality trends, message consistency, route plausibility)
Observe: results returned with confidence indicators
Decide: mark event type + severity; attach an audit trail; notify only if thresholds are met
Escalate: optional human review with a clear, editable incident narrative (controllable behavior)
What Clients Get (Product-Level Outcomes)
Integrity Alerts (Spoofing/Jamming Suspicion)
with an evidence summary, not a mystery warning
Confidence Scoring
"how sure are we?" based on multiple indicators and consistency checks
Incident Timeline
what changed, what checks ran, and what the system concluded (auditability)
Privacy Controls
role-based access, retention windows, and export controls for sensitive tracking data
Privacy & Confidentiality (Non-Negotiables)
collect what's needed for operations—not everything that's possible
Data minimization
collect what's needed for operations—not everything that's possible
Encryption
protect data in transit and at rest
Tenant isolation
reduce blast radius and prevent cross-client exposure
Client control
retention and access policies that match your environment
Ready to Secure Your Fleet?
Get in touch with our team to learn how GeoTRK can protect your tracking operations with enterprise-grade security and AI-powered integrity monitoring.