imu Class Reference

Rate integrating IMU model. More...

#include <imu.h>

Inherits dsim_model.

Public Member Functions
	imu (dsim_model_setup *setup)
	Constructor.
	~imu ()
	Destructor.
void	initialize_data ()
	Initialize outlets and output variables.
void	initialization_complete ()
	Finish initialization.
void	complete_timestep ()
	Computation done after numerical integration.
void	initialize_timestep ()
	Computation done prior to numerical integration.
void	rhs (double t, double jd)
	Computation done during numerical integration.

Protected Attributes
dsim_variable	bodyRate_dsim
	Body rate input.
dsim_variable	acceleration_dsim
	Linear acceleration input.
dsim_variable	angularAcceleration_dsim
	Angular acceleration input.
dsim_variable	centerOfMass_dsim
	Linear acceleration input.
dsim_variable	angularIncrement_dsim
	Angular increment input.
dsim_variable	acceleration_measured_dsim
	Linear acceleration input.
dsim_variable	integratedRate_dsim
	Angular rate input.
dsim_variable	biasAccelerometer_dsim
	Angular rate input.
dsim_variable	biasGyro_dsim
	Angular rate input.
dsim_variable	imuPosition_dsim
	Angular rate input.
dsim_variable	imuBodyToIMUMatrix_dsim
	Angular rate input.
dsim_variable	oneSigmaGyroBias_dsim
	Gyro random walk noise.
dsim_variable	oneSigmaAccelerometerBias_dsim
	Accelerometer random walk noise.
dsim_variable	oneSigmaGyro_dsim
	Gyro output noise.
dsim_variable	oneSigmaAccelerometer_dsim
	Accelerometer output noise.
dsim_variable	tauGyroBias_dsim
	Gyro bias time constant.
dsim_variable	tauAccelerometerBias_dsim
	Accelerometer bias time constant.
dsim_variable	omegaConstant_dsim
	Gyro constant rate.
dsim_variable	accelerationConstant_dsim
	Accelerometer constant rate.
dsim_variable	imuBodyToIMUMatrix_param_dsim
	Accelerometer constant rate.

Detailed Description

Rate integrating IMU model.

Models an inertial measurement unit with 3 orthogonal linear accelerometers and 3 rate integrating gyros. The model for both gyro and accelerometer is

omega = omega_true + bias + constant + noise dbias/dt + bias/tau = noise

The second equation is for a Markov model. If tau = infinity this is a random walk model.

Reference: Warren S. Flenniken IV, John H. Wall, David M. Bevly, "Characterization of Various IMU Error Sources and the Effect on Navigation Performance"

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The documentation for this class was generated from the following files:

• imu.h
• imu.cc