Quaternion estimation with vector matching and Kalman filter

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Description

Attitude quaternion estimation by means of complementary Kalman filter.

Usage

1
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