Navigation Lab: Understanding Inertial Drift, GNSS Satellite Geometry and Kalman Fusion

What is Inertial Drift?

Inertial drift refers to the error that accumulates over time in an INS system due to biases in its sensors. Accelerometers measure acceleration but can accumulate errors, while gyroscopes measure rotation rates with their own inaccuracies. These errors cause the estimated position and orientation of a vehicle or device to deviate from its actual path.

The drift is particularly noticeable when the INS system does not receive external updates from GNSS signals. Over time, this error can become significant, especially in long-haul navigation applications.

GNSS Satellite Geometry and DOP

Global Navigation Satellite Systems (GNSS) use multiple satellites to determine the position of a receiver on Earth. The geometry of these satellites relative to the receiver is crucial for determining accuracy, which is quantified by Dilution of Precision (DOP). DOP measures how well the satellite constellation can be used to determine position and velocity.

A lower DOP value indicates better geometric distribution of satellites, leading to higher positional accuracy. Factors such as the number of visible satellites, their relative positions in the sky, and the receiver’s location all influence DOP.

Kalman Fusion: Combining INS with GNSS

Kalman filtering is a mathematical method used to estimate the state of a system from noisy measurements. In the context of navigation, it combines data from inertial sensors (accelerometers and gyroscopes) with GNSS signals to provide more accurate position estimates.

The 15-state error-state Kalman filter in this lab accounts for various errors such as accelerometer bias, gyroscope bias, and other environmental factors. By fusing these measurements, the system can correct drift and improve overall accuracy.

Real-World Applications

The principles of inertial drift, GNSS satellite geometry, and Kalman fusion are applied in various fields such as aviation, maritime navigation, and autonomous vehicles. For instance, aircraft rely on these techniques to maintain precise flight paths during GPS outages or when flying over areas with poor satellite coverage.

In urban environments, the lab simulates scenarios where buildings can block GNSS signals, demonstrating how Kalman fusion helps mitigate these issues by leveraging inertial data.

Frequently asked questions

Try it live

Everything above runs in your browser — open Navigation Lab: Inertial Drift, GNSS Satellite Geometry (DOP) and Kalman Fusion and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.