IMU_Kalman-filter_MATLAB:使用MATLAB进行卡尔曼滤波（9轴）IMU数据

# IMU_Kalman-filter_MATLAB Kalman filtering (9-Axis) IMU data with MATLAB This is Kalman filter algorithm for 9-Axis IMU sensors. (Accelerometer, Gyroscope, Magnetometer) You can see graphically animated IMU sensor with data. # Testing https://youtu.be/NHTlb84UMCk # Demonstration -Will be added soon. # Features 1. Animated plot 2. Timeline 3. Hard-iron bias compensation 4. Angular velocity bias compensation # Description MAIN : Main code EKF : Main EKF function sampledata : sampledata I used # How to use **1. Get data from IMU sensor.** You should stay put IMU sensor for the first 5 seconds(It's for gyro compensation), then rotate it. I recommend you to rotate it several times. (It's good for magnetometer compensation.) **2. The data must look like this ↓ ** 'AccX AccY AccZ GyroX GyroY GyroZ MagX MagY MagZ time' for k=1 'AccX AccY AccZ GyroX GyroY GyroZ MagX MagY MagZ time' for k=2 . . . (time: IMU running time in ms) If you use arduino, you can copy from serial monitor, and paste it at sampledata.txt **3. Edit Initializing variables.** unit_transform_acc : Constant for LSB to N/m^2 unit_transform_gyro : Constant for LSB to rad/s Gyro_Compen_k : 1\~k'th data will be used for gyro compensation. I recommend 30 Mag_Compen_k : 1\~k'th data will be used for magnetometer compensation. I recommend Nsamples ref_mag : ref_mag'th data will be reference for psi. I recommend below 30 N_Q : N_Q * unit_matrix for Q matrix N_R : N_R * unit_matrix for R matrix N_P : N_P * unit_matrix for P matrix **4. Run** If the code is slow, you can annotate out the 'Graphical Plot & Dynamic Plot'. you will see only graph. # Closing If you have better idea or find error, please let me know. But, I'm not good at english, so I will be grateful if you explain it easily.