Surviving GNSS Denial: Free Inertial vs. Dead Reckoning Navigation
Share:
Published on:
Article Summary
- Free inertial navigation provides an available safety net during GNSS outages, however suffers from compounding position drift over time due to unaided IMU (Inertial Measurement Unit) sensor biases.
- Dead reckoning adds velocity aiding to reduce drift by fusing the Inertial Navigation System (INS) with velocity data to continuously constrain positioning errors.
- Transitioning from free inertial to dead reckoning is vital for autonomous systems to maintain prolonged mission accuracy in completely GNSS-denied environments.
Modern autonomous systems rely heavily on GNSS for absolute positioning, but satellite signals are inherently fragile.
Whether an autonomous haul truck is traversing a deep mine site or a defense Unmanned Aerial Vehicle (UAV) encounters active GNSS jamming, satellite signals are frequently lost.
To survive these outages and maintain operational integrity, systems rely on an INS. While an INS is self-contained, the specific navigation mode it operates in determines its long-term accuracy. For engineers integrating these systems, maintaining accurate positioning in a denied environment requires evaluating your navigation architecture. Relying on an unaided free inertial baseline is the lowest tier, while stepping up to dead reckoning is often a design necessity to control error rates.
The Baseline of Free Inertial Navigation
When a system operates in free inertial mode, it navigates without any external aiding (though it may sometimes incorporate a barometric pressure sensor for altitude). The system calculates its position purely through internal physics and the continuous double integration of acceleration data (calculating speed from acceleration, and distance from speed).
However, long-term navigation performance in a free inertial state is almost entirely dependent on the quality of the internal gyroscopes and accelerometers and the system’s algorithmic ability to estimate gyroscope biases. It serves as an important safety net for short-term GNSS dropouts, but it cannot sustain precise navigation over prolonged missions.
Ultimately, this means the system tracks its location entirely on its own by measuring its own movement, which works as a backup when GNSS is no longer available. However, small measurement errors gradually add up and cause the system to drift off course over time.
What is Dead Reckoning in Navigation?
Dead reckoning is the classical navigational concept of advancing a known position using estimated speed and course over time. In modern autonomous systems, dead reckoning is achieved by aiding the INS with relative velocity aiding sources – specific to the operational environment. Instead of relying purely on internal acceleration, the filter ingests external speed data to continuously cross-check and constrain the IMU’s drift.
Modalities Across Environments
- Land: For UGVs and autonomous platforms, dead reckoning land navigation is the standard approach to sustained autonomy in denied environments. When utilizing land navigation dead reckoning, the INS is historically aided by wheel odometry. The system measures the rotation of the vehicle’s wheels to establish an accurate relative velocity vector. For more complex missions, a Laser Velocity Sensor (LVS) can be used to extend the accuracy over extended distance.
- Air: In contested airspace, dead reckoning air navigation utilizes airspeed sensors or optical flow cameras to provide the INS with forward velocity data, keeping UAVs on target during prolonged electromagnetic warfare attacks.
- Sea: While the focus of modern autonomy is often on ground and aerospace, the mathematical principles originated from dead reckoning navigation at sea. In subsea environments where GNSS cannot penetrate, dead reckoning marine navigation utilizes acoustic sensors such as a Doppler Velocity Log (DVL) to track speed over the sea floor, applying the exact same principle of velocity aiding.
What Can Impact Dead Reckoning Accuracy
While velocity aiding flattens the error curve compared to a free inertial state, it introduces external variables. Engineers must account for what can impact dead reckoning accuracy in the field:
- In land systems, wheel slip in mud, sand, or ice means the odometry reports forward movement that isn’t actually happening.
- If the alignment between the aiding sensor (airspeed pitot tube or wheel encoder) and the IMU is not precisely calibrated, the system will integrate trajectory errors.
- Physical changes to the vehicle, such as a drop in tire pressure altering the wheel radius, will degrade the accuracy of the velocity data being fed to the INS.
Performance in the Field
| Feature | Free Inertial | Dead Reckoning |
| Data Sources | IMU only | IMU + Relative Velocity (Odometry, Airspeed) |
| Approximate Drift Profile | Quadratic error growth | Linear, tightly constrained error growth |
| Hardware Footprint | Self-contained | Requires external sensor integration |
| Best Use Case | Short bridges during short signal loss | Prolonged missions in GNSS-denied zones |
Resilient Navigation Beyond Free Inertial
Both navigation modes are critical components of a resilient autonomous stack. Free inertial provides the immediate, unjammable baseline required the second a signal is lost.
However, for autonomous systems that must operate continuously in tunnels, urban canyons, or contested environments, fusing external velocity data is mandatory. Dead reckoning is the design choice that transforms a temporarily blind vehicle into a fully operational, mission-capable system.
Executing this architecture effectively requires highly accurate hardware and intelligent software. This is where Advanced Navigation provides an advantage by combining high-grade IMUs with sensor fusion algorithms to integrate relative velocity sensors such as wheel odometers, airspeed sensors, or DVLs. Advanced Navigation’s filters are designed to optimize this dead reckoning data, characterizing and estimating gyroscope biases to minimise drift and ensure precise positioning no matter how long the GNSS outage lasts.
Equip your system to navigate GNSS dropouts seamlessly by finding the right Inertial Navigation System for your application.
FAQs
How does wheel slip affect dead reckoning accuracy in Uncrewed Ground Vehicles?
Wheel slip causes the odometry sensor to report false velocity data, which the INS will incorrectly integrate as forward movement, leading to compounded positional errors. To mitigate this, Advanced Navigation engineers use advanced filtering in our INS solutions to detect slip anomalies and dynamically reduce the weight of the odometry data during those events.
When should a UAV rely purely on free inertial mode instead of transitioning to dead reckoning?
A UAV should rely on free inertial navigation during highly dynamic maneuvers, brief GNSS dropouts lasting only a few seconds, or when strict SWaP-C constraints prohibit adding external airspeed sensors. For prolonged GNSS denial, integrating external velocity aiding for dead reckoning becomes mandatory to prevent the IMU’s exponential drift.
Recent Tech Articles
Surviving GNSS Denial: Free Inertial vs. Dead Reckoning Navigation
Learn what dead reckoning is in navigation and how it compares to free inertial mode for UGVs and UAVs operating during prolonged GNSS outages.
14 September 2026
Go to Article
The Threat of Spoofing and Building Resilient Navigation Systems
Protect assets from Global Navigation Satellite System (GNSS) spoofing. Explore multi-layered solutions delivered at scale.
31 August 2026
Go to Article