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# -*- coding: utf-8 -*- | |
import numpy as np | |
import matplotlib.pyplot as plt | |
def main(): | |
# 初期化 | |
T = 30 # 観測数 | |
r = 10.0 # 半径 | |
w = 1.0*10/180 * np.pi # 角速度[rad/s](公称値であり、データからこの値を推定する) |
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# -*- coding: utf-8 -*- | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from scipy.integrate import odeint | |
def main(): | |
x_ini = 0.0 # x(0) = 0 |
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# -*- coding: utf-8 -*- | |
import numpy as np | |
import matplotlib.pyplot as plt | |
def main(): | |
x_ini = 0.0 # x(0) = 0 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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# -*- coding: utf-8 -*- | |
import numpy as np | |
import matplotlib.pyplot as plt | |
def main(): | |
# 初期化 | |
T = 30 # 観測数 | |
r = 10.0 # 半径 | |
w = 1.0*10/180 * np.pi # 角速度[rad/s] |
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# -*- coding: utf-8 -*- | |
import numpy as np | |
import matplotlib.pyplot as plt | |
def lkf(T, Y, U, _X0, Sigma0, A, B, C, Q, R): | |
'''Linear Kalman Filter | |
- 状態方程式 | |
X(t+1) = A*X(t) + B*U(t) + V(t), V(t) ~ N(0,Q) | |
- 観測方程式 |