ahrs.LKF.VMATCH: Quaternion estimation with vector matching and Kalman filter

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

Attitude quaternion estimation by means of complementary Kalman filter.

Usage

ahrs.LKF.VMATCH(Filter, Sensors, q, Parameters)

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

Value

Filter	data structure for Linear Kalman Filter
Q	Correct quaternion

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.