irurueta-navigation-inertial

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irurueta-navigation-inertial is a Java library implementing strapdown inertial navigation, IMU calibration, and Earth magnetic field modeling for GNSS/INS integrated navigation systems. It provides the numerical building blocks that turn raw accelerometer, gyroscope and magnetometer readings into position, velocity and attitude estimates, and to fuse those estimates with GNSS measurements.

The library follows the equations and conventions described in Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems (Groves), and is organized around four packages:

Package Responsibility

com.irurueta.navigation.inertial

Core data containers such as BodyKinematics (specific forces and angular rates measured by an IMU) and frame/state representations shared by the other packages.

com.irurueta.navigation.inertial.estimators

Utility estimators for gravity, gravitation, kinematics, attitude leveling, position/velocity conversions and radii of curvature across NED, ECEF and ECI reference frames.

com.irurueta.navigation.inertial.navigators

Precision inertial navigation equation solvers that integrate IMU measurements over time to propagate position, velocity and attitude in NED, ECEF and ECI frames.

com.irurueta.navigation.inertial.calibration

Calibrators, error models and synthetic data generators used to estimate accelerometer, gyroscope and magnetometer biases, scale factors and cross-coupling errors, plus Kalman-filter-based INS and INS/GNSS state estimators.

com.irurueta.navigation.inertial.wmm

An implementation of the NOAA World Magnetic Model (WMM), used to estimate Earth’s magnetic flux density at a given location and epoch.

Core concept: strapdown inertial navigation

Strapdown inertial navigation integrates IMU measurements over time to keep track of a body’s attitude, velocity and position without external references. The library’s navigators (NEDInertialNavigator, ECEFInertialNavigator) implement the following update loop:

flowchart TD A[Gyroscope angular rates] --> B[Attitude update:\nintegrate rotation] C[Accelerometer specific forces] --> D[Transform specific forces\nto navigation frame using attitude] B --> D D --> E[Velocity update:\nintegrate specific force minus gravity] E --> F[Position update:\nintegrate velocity] F --> G[Updated position, velocity, attitude] G -->|next epoch| A

When raw IMU integration alone is used, small sensor errors accumulate into drift over time. To bound this drift, the library also provides Kalman-filter-based estimators:

  • INSLooselyCoupledKalmanFilteredEstimator propagates INS state and its uncertainty on its own.

  • INSGNSSLooselyCoupledKalmanFilteredEstimator fuses a separately-computed GNSS position/velocity solution into the INS propagation as measurement updates.

  • INSGNSSTightlyCoupledKalmanFilteredEstimator feeds raw GNSS measurements directly into the same filter, allowing it to work even with a partial satellite geometry.

Core concept: sensor calibration

Before an IMU’s measurements can be trusted, its systematic errors (biases, scale factors, cross-axis coupling) must be estimated and removed. The calibration package (and its accelerometer, gyroscope, magnetometer, bias, generators, intervals and noise sub-packages) implements calibrators that consume measurements collected at one or more known frames/orientations and produce an IMUErrors-style correction model that can be applied to raw sensor readings before they are handed to a navigator.

Core concept: Earth magnetic field modeling

WMMEarthMagneticFluxDensityEstimator, backed by the bundled NOAA WMM coefficient file and WorldMagneticModel, estimates the Earth’s magnetic flux density (in the NED frame) at a given latitude, longitude, height and date. This is used both as an independent heading reference and as an input to magnetometer calibration.

Where to go next

  • Installation — add this library as a dependency to your project.

  • Reference — links to generated reports (Javadoc, coverage, quality) and external dashboards.