本文整理汇总了Python中filterpy.kalman.KalmanFilter.H[i,i]方法的典型用法代码示例。如果您正苦于以下问题：Python KalmanFilter.H[i,i]方法的具体用法？Python KalmanFilter.H[i,i]怎么用？Python KalmanFilter.H[i,i]使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类filterpy.kalman.KalmanFilter的用法示例。
在下文中一共展示了KalmanFilter.H[i,i]方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: kinematic_kf
# 需要导入模块: from filterpy.kalman import KalmanFilter [as 别名]
# 或者: from filterpy.kalman.KalmanFilter import H[i,i] [as 别名]
def kinematic_kf(dim, order, dt=1., order_by_dim=True):
""" Returns a KalmanFilter using newtonian kinematics for an arbitrary
number of dimensions and order. So, for example, a constant velocity
filter in 3D space would be created with
kinematic_kf(3, 1)
which will set the state `x` to be interpreted as
[x, x', y, y', z, z'].T
If you set `order_by_dim` to False, then `x` is assumed to be
[x y z x' y' z'].T
As another example, a 2D constant jerk is created with
kinematic_kf(2, 3)
Assumes that the measurement z is position in each dimension. If this is not
true you will have to alter the H matrix by hand.
P, Q, R are all set to the Identity matrix.
H is assigned assuming the measurement is position, one per dimension `dim`.
Parameters
----------
dim : int
number of dimensions
order : int, >= 1
order of the filter. 2 would be a const acceleration model.
dim_z : int, default 1
size of z vector *per* dimension `dim`. Normally should be 1
dt : float, default 1.0
Time step. Used to create the state transition matrix
"""
dim_x = order + 1
kf = KalmanFilter(dim_x=dim * dim_x, dim_z=dim)
F = kinematic_state_transition(order, dt)
if order_by_dim:
diag = [F] * dim
kf.F = sp.linalg.block_diag(*diag)
else:
kf.F.fill(0.0)
for i, x in enumerate(F.ravel()):
f = np.eye(dim) * x
ix, iy = (i // dim_x) * dim, (i % dim_x) * dim
kf.F[ix:ix+dim, iy:iy+dim] = f
if order_by_dim:
for i in range(dim):
kf.H[i, i * dim_x] = 1.
else:
for i in range(dim):
kf.H[i, i] = 1.
return kf