Need Help with Implementing a Kalman Filter in MATLAB for Sensor Fusion

Hello MATLAB community,

I'm working on a project that involves sensor fusion, and I'm looking to implement a Kalman filter in MATLAB to improve the accuracy of my sensor data. I have some experience with MATLAB, but I'm relatively new to Kalman filters.

Specifically, I need to fuse data from an accelerometer and a gyroscope to estimate the orientation of an object. Can anyone provide guidance or share some example code on how to implement a Kalman filter for sensor fusion in MATLAB?

Thank you in advance!

Pursuing ruby on rails certification

Answer 1

[William Rose]

@Keshav Sharma,

See the Matlab help for imufilter(). See general Matlab help for this problem here.

A simple example is below. It gets data from a file with 7 columns of data: time, acceleration (X,Y,Z), angular velocity (X,Y,Z)

d=importdata('accGyroSim1.csv');
accelReadings=d.data(:,2:4);                % acceleration data x,y,z (m/s^2)
gyroReadings=d.data(:,5:7)*pi/180;          % angular velocity data x,y,z (rad/s)
fs=512;                                     % sampling rate (Hz)
FUSE = imufilter('SampleRate',fs);          % create IMU filter object
[orientation,angularVelocity] = FUSE(accelReadings,gyroReadings);   % use filter object to esitmate orientation and angular velocity

This returns a 3x3xM array of orientations and a Mx3 array of estimated angular velocities.

Good luck.