ahrs.LKF.QUATERNION: LKF-based AHRS algorithm

In RAHRS: Data Fusion Filters for Attitude Heading Reference System (AHRS) with Several Variants of the Kalman Filter and the Mahoney and Madgwick Filters

Description

Implementation of the LKF-based AHRS algorithm based on measurements from three-component accelerometer with orthogonal axes, vector magnetometer and three-axis gyroscope. Estimates the current quaternion attitude.

Usage

ahrs.LKF.QUATERNION(Filter, Sensors, q, Parameters, dw)

Arguments

Filter	data structure for Linear Kalman Filter Filter.x State vector [3x1] Filter.P Covariance matrix [3x3] Filter.Q System noise matrix [3x3] Filter.R Measurement noise matrix [6x6]
Sensors	sensors data structure Sensors.w current calibrated gyroscope measurement [3x1], rad/sec Sensors.a current calibrated accelerometer measurement [3x1], g Sensors.m current calibrated magnetometer measurement [3x1], |m| = 1
q	quaternion
Parameters	AHRS Parameters Parameters.mn Magnetic Field Vector In Navigation Frame [3x1], |m| = 1 Parameters.an Acceleration vector In Navigation Frame [3x1], g Parameters.dt Sampling period, 1/Hz
dw	angular rate

Value

Filter	Data structure for Linear Kalman Filter
Q	Correct quaternion
dw	Correct angular rate

Author(s)

Jose Gama

References

Vlad Maximov, 2012 Scalar Calibration of Vector accelerometers and magnetometers, GyroLib documentation

RAHRS documentation built on May 2, 2019, 2:42 a.m.