diff --git a/controls/MATLAB/IMU/IMU_dataAnalysis_allFilters/analyzeIMU_data.m b/controls/MATLAB/IMU/IMU_dataAnalysis_allFilters/analyzeIMU_data.m
new file mode 100644
index 0000000000000000000000000000000000000000..819117c5489e9c503c171d031f8edeacb780b545
--- /dev/null
+++ b/controls/MATLAB/IMU/IMU_dataAnalysis_allFilters/analyzeIMU_data.m
@@ -0,0 +1,273 @@
+function analyzeIMU_data( fileName )
+%UNTITLED2 Summary of this function goes here
+% Detailed explanation goes here
+
+
+ IMU_dataDirectory = 'C:\Users\Tara\Desktop\Project Documents\Current Project Documents\EE 492\Data\IMU Data';
+ IMU_dataFile = [IMU_dataDirectory , '\' , fileName];
+
+ IMU_data = readtable(IMU_dataFile);
+
+ g = 9.8; %meters per second squared
+
+ %Set the column values of the table
+ timeCol = 1;
+ accelXCol = 2;
+ accelYCol = 3;
+ accelZCol = 4;
+ gyroXCol = 5;
+ gyroYCol = 6;
+ gyroZCol = 7;
+
+ %Pull the data columns into arrays
+ timeArray = IMU_data{:, timeCol};
+ accelXArray = IMU_data{:, accelXCol};
+ accelYArray = IMU_data{:, accelYCol};
+ accelZArray = IMU_data{:, accelZCol};
+ gyroXArray = IMU_data{:, gyroXCol};
+ gyroYArray = IMU_data{:, gyroYCol};
+ gyroZArray = IMU_data{:, gyroZCol};
+
+ %Get the mean and standard deviation of the accelerometer data and multiply
+ %by the g constant to get this value in meters per second
+ accelX_mean = mean(accelXArray) * g;
+ accelY_mean = mean(accelYArray) * g;
+ accelZ_mean = mean(accelZArray) * g
+ accelX_dev = std(accelXArray) * g;
+ accelY_dev = std(accelYArray) * g;
+ accelZ_dev = std(accelZArray) * g;
+
+ %Get mean and standard deviation of gyroscope data, which is in rad / sec
+ gyroX_mean = mean(gyroXArray);
+ gyroY_mean = mean(gyroYArray);
+ gyroZ_mean = mean(gyroZArray);
+ gyroX_dev = std(gyroXArray);
+ gyroY_dev = std(gyroYArray);
+ gyroZ_dev = std(gyroZArray);
+
+ %Calculate the variances from the standard deviations
+ accelX_var = accelX_dev ^ 2;
+ accelY_var = accelY_dev ^ 2;
+ accelZ_var = accelZ_dev ^ 2;
+ gyroX_var = gyroX_dev ^ 2;
+ gyroY_var = gyroY_dev ^ 2;
+ gyroZ_var = gyroZ_dev ^ 2;
+
+ %Print received data from accelerometer nicely to command prompt
+ disp( ' ' );
+ disp('Accelerometer data: ');
+ disp( [ 'X acceleration mean ( m / s^2 ): ' , num2str(accelX_mean) ] );
+ disp( [ 'Y acceleration mean ( m / s^2 ): ' , num2str(accelY_mean) ] );
+ disp( [ 'Z acceleration mean ( m / s^2 ): ' , num2str(accelZ_mean) ] );
+ disp( [ 'X acceleration standard deviation ( m / s^2 ): ' , num2str(accelX_dev) ] );
+ disp( [ 'Y acceleration standard deviation ( m / s^2 ): ' , num2str(accelY_dev) ] );
+ disp( [ 'Z acceleration standard deviation ( m / s^2 ): ' , num2str(accelZ_dev) ] );
+ disp( [ 'X acceleration variance ( (m/s^2) ^ 2 ): ', num2str(accelX_var) ] );
+ disp( [ 'Y acceleration variance ( (m/s^2) ^ 2 ): ', num2str(accelY_var) ] );
+ disp( [ 'Z acceleration variance ( (m/s^2) ^ 2 ): ', num2str(accelZ_var) ] );
+ disp( ' ' );
+
+
+ %Print received data from gyroscope nicely to command prompt
+ disp('Gyroscope data: ');
+ disp( [ 'Angular velocity about X mean ( rad / s ): ' , num2str(gyroX_mean) ] );
+ disp( [ 'Angular velocity about Y mean ( rad / s ): ' , num2str(gyroY_mean) ] );
+ disp( [ 'Angular velocity about Z mean ( rad / s ): ' , num2str(gyroZ_mean) ] );
+ disp( [ 'Angular velocity about X standard deviation ( rad / s ): ' , num2str(gyroX_dev) ] );
+ disp( [ 'Angular velocity about Y standard deviation ( rad / s ): ' , num2str(gyroY_dev) ] );
+ disp( [ 'Angular velocity about Z standard deviation ( rad / s ): ' , num2str(gyroZ_dev) ] );
+ disp( [ 'Angular velocity about X variance ( (rad/s) ^ 2 ): ', num2str(gyroX_var) ] );
+ disp( [ 'Angular velocity about Y variance ( (rad/s) ^ 2 ): ', num2str(gyroY_var) ] );
+ disp( [ 'Angular velocity about Z variance ( (rad/s) ^ 2 ): ', num2str(gyroZ_var) ] );
+ disp( ' ' );
+
+ %Graph plots:
+ figure();
+ plot(timeArray, accelXArray * g);
+ title('X Acceleration Data ( m / s^2 )');
+ xlabel('Time (s)');
+
+ figure();
+ plot(timeArray, accelYArray * g);
+ title('Y Acceleration Data ( m / s^2 )');
+ xlabel('Time (s)');
+
+ figure();
+ plot(timeArray, accelZArray * g);
+ title('Z Acceleration Data ( m / s^2 )');
+ xlabel('Time (s)');
+
+ figure();
+ plot(timeArray, gyroXArray);
+ title('Angular Velocity about X Data (rad/s)');
+ xlabel('Time (s)');
+
+ figure();
+ plot(timeArray, gyroYArray);
+ title('Angular Velocity about Y Data (rad/s)');
+ xlabel('Time (s)');
+
+ figure();
+ plot(timeArray, gyroZArray);
+ title('Angular Velocity about Z Data (rad/s)');
+ xlabel('Time (s)');
+
+
+ %Get the magnitude of the accelerometer data
+ mag_accel = sqrt( accelXArray.^2 + accelYArray.^2 + accelZArray.^2 ) * g;
+ mag_accel_mean = mean( mag_accel );
+ mag_accel_dev = std( mag_accel );
+ mag_accel_var = mag_accel_dev ^ 2;
+
+ %Display these results and graph them
+ disp( ['Magnitude of acceleration vector mean ( m / s^2 ): ' , num2str(mag_accel_mean) ] );
+ disp( ['Magnitude of acceleration vector standard deviation ( m / s^2 ): ' , num2str(mag_accel_dev) ] );
+ disp( ['Magnitude of acceleration vector variance ( ( m/s^2 )^2 ): ' , num2str(mag_accel_var) ] );
+ figure();
+ plot( timeArray, mag_accel, 'b' );
+ hold on;
+ plot( timeArray, ones(1, length(timeArray)) * g , 'r-' );
+ title('Magnitude of Acceleration Vector with Respect to Nominal ( m / s^2 )');
+ xlabel('Time (s)');
+ legend( 'Accel Mag Measured' , 'Accel Mag Nominal' );
+
+ %Also graph the percent error
+ mag_accel_percentError = abs(mag_accel - g) / g * 100;
+ figure();
+ plot( timeArray, mag_accel_percentError);
+ title('Magnitude of Acceleration Vector Percent Error from Nominal (%) ');
+ xlabel('Time (s)');
+ ylabel('Percent Error (%)');
+
+ mag_accel_percentError_mean = mean( mag_accel_percentError );
+ mag_accel_percentError_dev = std( mag_accel_percentError );
+
+ disp(' ');
+ disp( ['Percent error from nominal mean ( % ): ' , num2str(mag_accel_percentError_mean) ] );
+ disp( ['Percent error from nominal standard deviation ( % ): ' , num2str(mag_accel_percentError_dev) ] );
+ disp(' ');
+
+
+
+
+
+ %Following example given in MATLAB help
+ %Looking at the frequency response characterisitcs of the noise
+ timeArray_diff = diff( timeArray );
+ timeArray_diffMean = mean( timeArray_diff ); %seconds
+ fs = 1 / timeArray_diffMean;
+
+ %Get the fast fourier transform
+ accelX_fft = fft( accelXArray*g - accelX_mean );
+ accelY_fft = fft( accelYArray*g - accelY_mean );
+ accelZ_fft = fft( accelZArray*g - accelZ_mean );
+ gyroX_fft = fft( gyroXArray - gyroX_mean );
+ gyroY_fft = fft( gyroYArray - gyroY_mean );
+ gyroZ_fft = fft( gyroZArray - gyroZ_mean );
+
+ %Determine the length of our data and obtain only first half of FFT data
+ N = length(timeArray);
+ accelX_fft = accelX_fft( 1:N/2+1 );
+ accelY_fft = accelY_fft( 1:N/2+1 );
+ accelZ_fft = accelZ_fft( 1:N/2+1 );
+ gyroX_fft = gyroX_fft( 1:N/2+1 );
+ gyroY_fft = gyroY_fft( 1:N/2+1 );
+ gyroZ_fft = gyroZ_fft( 1:N/2+1 );
+
+ %Get the power spectral density of the signal
+ accelX_PSD = ( 1 / (2*pi*N) ) * abs( accelX_fft ) .^ 2;
+ accelY_PSD = ( 1 / (2*pi*N) ) * abs( accelY_fft ) .^ 2;
+ accelZ_PSD = ( 1 / (2*pi*N) ) * abs( accelZ_fft ) .^ 2;
+ gyroX_PSD = ( 1 / (2*pi*N) ) * abs( gyroX_fft ) .^ 2;
+ gyroY_PSD = ( 1 / (2*pi*N) ) * abs( gyroY_fft ) .^ 2;
+ gyroZ_PSD = ( 1 / (2*pi*N) ) * abs( gyroZ_fft ) .^ 2;
+
+ %Multiply everything but the first and last term by 2
+ accelX_PSD(2:end-1) = 2*accelX_PSD(2:end-1);
+ accelY_PSD(2:end-1) = 2*accelY_PSD(2:end-1);
+ accelZ_PSD(2:end-1) = 2*accelZ_PSD(2:end-1);
+ gyroX_PSD(2:end-1) = 2*gyroX_PSD(2:end-1);
+ gyroY_PSD(2:end-1) = 2*gyroY_PSD(2:end-1);
+ gyroZ_PSD(2:end-1) = 2*gyroZ_PSD(2:end-1);
+
+ %Get the frequency range vector
+ freq = 0:(2*pi)/N:pi;
+
+ %Show the plots of the power spectral density in the log domain
+ figure();
+ plot( freq/pi, 10*log10(accelX_PSD) );
+ %plot( freq/pi, accelX_PSD, '*' );
+ title('Periodogram of X Acceleration Data Using FFT');
+ xlabel('Normalized Frequency (\times\pi rad/sample)');
+ ylabel('Power Frequency (dB/rad/sample)');
+ %ylabel('Power Frequency');
+
+ figure();
+ plot( freq/pi, 10*log10(accelY_PSD) );
+ title('Periodogram of Y Acceleration Data Using FFT');
+ xlabel('Normalized Frequency (\times\pi rad/sample)');
+ ylabel('Power Frequency (dB/rad/sample)');
+
+ figure();
+ plot( freq/pi, 10*log10(accelZ_PSD) );
+ title('Periodogram of Z Acceleration Data Using FFT');
+ xlabel('Normalized Frequency (\times\pi rad/sample)');
+ ylabel('Power Frequency (dB/rad/sample)');
+
+ figure();
+ plot( freq/pi, 10*log10(gyroX_PSD) );
+ title('Periodogram of Angular Velocity about X Data Using FFT');
+ xlabel('Normalized Frequency (\times\pi rad/sample)');
+ ylabel('Power Frequency (dB/rad/sample)');
+
+ figure();
+ plot( freq/pi, 10*log10(gyroY_PSD) );
+ title('Periodogram of Angular Velocity about Y Using FFT');
+ xlabel('Normalized Frequency (\times\pi rad/sample)');
+ ylabel('Power Frequency (dB/rad/sample)');
+
+ figure();
+ plot( freq/pi, 10*log10(gyroZ_PSD) );
+ title('Periodogram of Angular Velocity about Z Using FFT');
+ xlabel('Normalized Frequency (\times\pi rad/sample)');
+ ylabel('Power Frequency (dB/rad/sample)');
+
+
+
+ %Estimate the average power spectral density, ignoring the first value
+ accelX_PSD_mean = mean( accelX_PSD(2:end) );
+ accelX_PSD_mean_dB = 10*log10( accelX_PSD_mean );
+ accelY_PSD_mean = mean( accelY_PSD(2:end) );
+ accelY_PSD_mean_dB = 10*log10( accelY_PSD_mean );
+ accelZ_PSD_mean = mean( accelZ_PSD(2:end) );
+ accelZ_PSD_mean_dB = 10*log10( accelZ_PSD_mean );
+ gyroX_PSD_mean = mean( gyroX_PSD(2:end) );
+ gyroX_PSD_mean_dB = 10*log10( gyroX_PSD_mean );
+ gyroY_PSD_mean = mean( gyroY_PSD(2:end) );
+ gyroY_PSD_mean_dB = 10*log10( gyroY_PSD_mean );
+ gyroZ_PSD_mean = mean( gyroZ_PSD(2:end) );
+ gyroZ_PSD_mean_dB = 10*log10( gyroZ_PSD_mean );
+
+ %Display the average power spectral densities in dB
+ disp( ' ' );
+ disp( [ 'Average PSD for X Acceleration Data Noise (dB): ' , num2str(accelX_PSD_mean_dB) ] );
+ disp( [ 'Average PSD for Y Acceleration Data Noise (dB): ' , num2str(accelY_PSD_mean_dB) ] );
+ disp( [ 'Average PSD for Z Acceleration Data Noise (dB): ' , num2str(accelZ_PSD_mean_dB) ] );
+ disp( [ 'Average PSD for Angular Velocity about X Data Noise (dB): ' , num2str(gyroX_PSD_mean_dB) ] );
+ disp( [ 'Average PSD for Angular Velocity about Y Data Noise (dB): ' , num2str(gyroY_PSD_mean_dB) ] );
+ disp( [ 'Average PSD for Angular Velocity about Z Data Noise (dB): ' , num2str(gyroZ_PSD_mean_dB) ] );
+ disp( ' ' );
+
+ disp( ' ' );
+ disp( [ 'Average PSD for X Acceleration Data Noise : ' , num2str(accelX_PSD_mean) ] );
+ disp( [ 'Average PSD for Y Acceleration Data Noise : ' , num2str(accelY_PSD_mean) ] );
+ disp( [ 'Average PSD for Z Acceleration Data Noise : ' , num2str(accelZ_PSD_mean) ] );
+ disp( [ 'Average PSD for Angular Velocity about X Data Noise : ' , num2str(gyroX_PSD_mean) ] );
+ disp( [ 'Average PSD for Angular Velocity about Y Data Noise : ' , num2str(gyroY_PSD_mean) ] );
+ disp( [ 'Average PSD for Angular Velocity about Z Data Noise : ' , num2str(gyroZ_PSD_mean) ] );
+ disp( ' ' );
+
+
+
+end
+
diff --git a/controls/MATLAB/IMU/IMU_dataAnalysis_allFilters/analyzeIMU_data_allFilters.m b/controls/MATLAB/IMU/IMU_dataAnalysis_allFilters/analyzeIMU_data_allFilters.m
new file mode 100644
index 0000000000000000000000000000000000000000..8570c8a7f4061607265762fbe65ecb264f740a09
--- /dev/null
+++ b/controls/MATLAB/IMU/IMU_dataAnalysis_allFilters/analyzeIMU_data_allFilters.m
@@ -0,0 +1,11 @@
+%Run through IMU data for all filters (levels 0 to 6):
+
+% for i = 0:1:6
+%
+% fileName = [ 'level' , num2str(i) , '.csv' ];
+% analyzeIMU_data( fileName );
+%
+% end
+
+fileName = 'level5.csv';
+analyzeIMU_data( fileName );
\ No newline at end of file