Python 3.x 卡尔曼滤波器基本应用/学习-代码似乎非常慢
我有一个旋转的平台和一个测量其位置的传感器。我试图编程一个简单的卡尔曼滤波器来平滑测量。我的测量运行长度大约在10000-15000个数据点之间。模拟时间超过3分钟 我希望代码会更快,因为kalman用于实时应用程序。另外,我用测量值做的其他计算几乎不需要那么长的时间,而且几乎是即时的。或者这是正常的,因为矩阵运算 我主要使用这个示例作为模板。下面是我的代码,也许有可能用不同的方式来编写,以加快速度Python 3.x 卡尔曼滤波器基本应用/学习-代码似乎非常慢,python-3.x,pandas,numpy,kalman-filter,Python 3.x,Pandas,Numpy,Kalman Filter,我有一个旋转的平台和一个测量其位置的传感器。我试图编程一个简单的卡尔曼滤波器来平滑测量。我的测量运行长度大约在10000-15000个数据点之间。模拟时间超过3分钟 我希望代码会更快,因为kalman用于实时应用程序。另外,我用测量值做的其他计算几乎不需要那么长的时间,而且几乎是即时的。或者这是正常的,因为矩阵运算 我主要使用这个示例作为模板。下面是我的代码,也许有可能用不同的方式来编写,以加快速度 import numpy as np import matplotlib.pyplot as p
import numpy as np
import matplotlib.pyplot as plt
x = np.matrix([[0.0, 0.0]]).T # initial state vector x and x_point
P = np.diag([1.0, 1.0]) #!!! initial uncertainty
H = np.matrix([[1.0, 0.0]]) # Measurement matrix H
StD = 20 # Standard deviation of the sensor
R = StD**2 # Measurment Noise Covariance
sv = 2 # process noise basically through possible acceleration
I = np.eye(2) # Identity matrix
for n in range(len(df.loc[[name], ['Time Delta']])):
# Time Update (Prediction)
# ========================
# Update A and Q with correct timesteps
dt = float(df.loc[[name], ['Time Delta']].values[n])
A = np.matrix([[1.0, dt],
[0.0, 1.0]])
G = np.matrix([dt**2/2,dt]).T #!!! np.matrix([[dt**2/2], [dt]]) # Process Noise
Q = G*G.T*sv**2 # Process Noise Covariance Q
# Project the state ahead
x = A*x # not used + B*u
# Project the error covariance ahead
P = A*P*A.T + Q
# Measurement Update (Correction)
# ===============================
# Compute the Kalman Gain
S = H*P*H.T + R
K = (P*H.T) * S**-1 #!!! Use np.linalg.pinv(S) instead of S**-1 if S is a matrix
# Update the estimate via z
Z = df.loc[[name], ['HMC Az Relative']].values[n]
y = Z - (H*x)
x = x + (K*y)
# Update the error covariance
P = (I - (K*H))*P
我找到了答案
在for迭代中为dt和Z调用pandas“address”使代码变得非常慢。我为dt和z数组创建了两个新变量,现在我的代码几乎是即时的。
帮助我认识到我的问题所在。对于任何读到这篇文章并有类似问题的人来说,问这样的问题也会是一个更好的地方
import numpy as np
import matplotlib.pyplot as plt
x = np.matrix([[0.0, 0.0]]).T # initial state vector x and x_point
P = np.diag([1.0, 1.0]) #!!! initial uncertainty
H = np.matrix([[1.0, 0.0]]) # Measurement matrix H
StD = 20 # Standard deviation of the sensor
R = StD**2 # Measurment Noise Covariance
sv = 2 # process noise basically through possible acceleration
I = np.eye(2) # Identity matrix
timesteps = df.loc[[name], ['Time Delta']].values
measurements = df.loc[[name], ['HMC Az Relative']].values
for n in range(len(df.loc[[name], ['Time Delta']])):
# Time Update (Prediction)
# ========================
# Update A and Q with correct timesteps
dt = timesteps[n]
A = np.matrix([[1.0, dt],
[0.0, 1.0]])
G = np.matrix([dt**2/2,dt]).T #!!! np.matrix([[dt**2/2], [dt]]) # Process Noise
Q = G*G.T*sv**2 # Process Noise Covariance Q
# Project the state ahead
x = A*x # not used + B*u
# Project the error covariance ahead
P = A*P*A.T + Q
# Measurement Update (Correction)
# ===============================
# Compute the Kalman Gain
S = H*P*H.T + R
K = (P*H.T) * S**-1 #!!! Use np.linalg.pinv(S) instead of S**-1 if S is a matrix
# Update the estimate via z
Z = measurements[n]
y = Z - (H*x)
x = x + (K*y)
# Update the error covariance
P = (I - (K*H))*P