From ac8d1788e4b350de986d8ba149c915241369a5b5 Mon Sep 17 00:00:00 2001 From: Mateusz Tylka Date: Fri, 18 Aug 2023 11:51:44 +0200 Subject: [PATCH] data input correction and implement butter_lowpass_filter --- 12_EulerEKF_pitch.png | Bin 32074 -> 34597 bytes 12_EulerEKF_roll.png | Bin 31584 -> 31428 bytes euler_ekf.py | 54 ++++++++++++++++++++++++++++++++++++------ 3 files changed, 47 insertions(+), 7 deletions(-) diff --git a/12_EulerEKF_pitch.png b/12_EulerEKF_pitch.png index f30a2f6d529698467ff402afeaeeec252edb4dbf..a5b0d02195c15df5303eb381c7155dd90bbe548a 100644 GIT binary patch literal 34597 zcmeEuFjZI|QVWknZkx_`Uc8 z-lzBBF@`!W=iGhv-e;}7)|zuJ!<7`?W1zi20{{R+T1s3Q01)5+09kp44E~0Dd|@5@ zhu=j)(?!MJ%*FkqlPMto(Z#{W-o?h!h|!c z+eN>m4%yWo4vp*`Y~FiqATz@G>r$ubf#aqy?J8~7X+ z7DoS*4S4l*fei+PgP*BjP%-dh?z3P2|MLGU+ai}Rjq%KsCmn-MAwx`A83%?OG(G$h zoE_`4KzMF06_>>rAZcv!?Y-9!w&;e8|maKO(JME~6)jvWG_e7gMUqPDNhPk4pwk%TqpW7(4JvLOk`qWdx%k!}G%EjfZuLh9@JB 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+def butter_lowpass_filter(data, cutoff, fs, order): + nyq = (0.5 * fs) # Nyquist Frequency + normal_cutoff = cutoff / nyq + # Get the filter coefficients + b, a = butter(order, normal_cutoff, btype='low', analog=False) + y = filtfilt(b, a, data) + return y + def normalize_data_vector(data, division): return [i/division for i in list(data)] H, Q, R = None, None, None x, P = None, None firstRun = True + division_param = 32768 +# Filter requirements. +T = 5.0 # Sample Period +fs = 30.0 # sample rate, Hz +cutoff = 2 # desired cutoff frequency of the filter, Hz, slightly higher than actual 1.2 Hz +order = 2 # sin wave can be approx represented as quadratic + df = pd.read_csv('raw_data_6d.xls', sep='\t') -gyroX = normalize_data_vector(df['%GyroX'].values, division_param) -gyroY = normalize_data_vector(df['%GyroX'].values, division_param) -gyroZ = normalize_data_vector(df['%GyroX'].values, division_param) +gyroX = df['%GyroX'].values +gyroY = df['GyroY'].values +gyroZ = df['GyroZ'].values -acceX = normalize_data_vector(df['AcceX'].values, division_param) -acceY = normalize_data_vector(df['AcceY'].values, division_param) -acceZ = normalize_data_vector(df['AcceZ'].values, division_param) +acceX = df['AcceX'].values +acceY = df['AcceY'].values +acceZ = df['AcceZ'].values + +gyroX = normalize_data_vector(gyroX, division_param) +gyroY = normalize_data_vector(gyroY, division_param) +gyroZ = normalize_data_vector(gyroZ, division_param) + +acceX = normalize_data_vector(acceX, division_param) +acceY = normalize_data_vector(acceY, division_param) +acceZ = normalize_data_vector(acceZ, division_param) + +# gyroX = butter_lowpass_filter(gyroX, cutoff, fs, order) +# gyroY = butter_lowpass_filter(gyroY, cutoff, fs, order) +# gyroZ = butter_lowpass_filter(gyroZ, cutoff, fs, order) + +# acceX = butter_lowpass_filter(acceX, cutoff, fs, order) +# acceY = butter_lowpass_filter(acceY, cutoff, fs, order) +# acceZ = butter_lowpass_filter(acceZ, cutoff, fs, order) gyro_data = [gyroX, gyroY, gyroZ] acce_data = [acceX, acceY, acceZ] @@ -146,4 +178,12 @@ plt.subplot(133) plt.plot(t, PsiSaved) plt.xlabel('Time [Sec]') plt.ylabel('Psi angle [deg]') -''' \ No newline at end of file +''' + +n =int(T * fs) +# sin wave +sig = np.sin(1.2*2*np.pi*n)# Lets add some noise +noise = 1.5*np.cos(9*2*np.pi*n) + 0.5*np.sin(12.0*2*np.pi*n) +data = sig + noise + +print(data)