本文整理汇总了Python中filterpy.kalman.KalmanFilter.rts_smoother方法的典型用法代码示例。如果您正苦于以下问题：Python KalmanFilter.rts_smoother方法的具体用法？Python KalmanFilter.rts_smoother怎么用？Python KalmanFilter.rts_smoother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.rts_smoother方法的4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: test_rts
# 需要导入模块: from filterpy.kalman import KalmanFilter [as 别名]
# 或者: from filterpy.kalman.KalmanFilter import rts_smoother [as 别名]
def test_rts():
fk = KalmanFilter(dim_x=2, dim_z=1)
fk.x = np.array([-1., 1.]) # initial state (location and velocity)
fk.F = np.array([[1.,1.],
[0.,1.]]) # state transition matrix
fk.H = np.array([[1.,0.]]) # Measurement function
fk.P = .01 # covariance matrix
fk.R = 5 # state uncertainty
fk.Q = 0.001 # process uncertainty
zs = [t + random.randn()*4 for t in range(40)]
mu, cov, _, _ = fk.batch_filter (zs)
mus = [x[0] for x in mu]
M, P, _, _ = fk.rts_smoother(mu, cov)
if DO_PLOT:
p1, = plt.plot(zs,'cyan', alpha=0.5)
p2, = plt.plot(M[:,0],c='b')
p3, = plt.plot(mus,c='r')
p4, = plt.plot([0, len(zs)], [0, len(zs)], 'g') # perfect result
plt.legend([p1, p2, p3, p4],
["measurement", "RKS", "KF output", "ideal"], loc=4)
plt.show()示例2: one_run_test_fls
# 需要导入模块: from filterpy.kalman import KalmanFilter [as 别名]
# 或者: from filterpy.kalman.KalmanFilter import rts_smoother [as 别名]
def one_run_test_fls():
fls = FixedLagSmoother(dim_x=2, dim_z=1)
fls.x = np.array([0., .5])
fls.F = np.array([[1.,1.],
[0.,1.]])
fls.H = np.array([[1.,0.]])
fls.P *= 200
fls.R *= 5.
fls.Q *= 0.001
kf = KalmanFilter(dim_x=2, dim_z=1)
kf.x = np.array([0., .5])
kf.F = np.array([[1.,1.],
[0.,1.]])
kf.H = np.array([[1.,0.]])
kf.P *= 2000
kf.R *= 1.
kf.Q *= 0.001
N = 4 # size of lag
nom = np.array([t/2. for t in range (0,40)])
zs = np.array([t + random.randn()*1.1 for t in nom])
xs, x = fls.smooth_batch(zs, N)
M, P, *_ = kf.batch_filter(zs)
rts_x, *_ = kf.rts_smoother(M, P)
xfl = xs[:,0].T[0]
xkf = M[:,0].T[0]
fl_res = abs(xfl-nom)
kf_res = abs(xkf-nom)
if DO_PLOT:
plt.cla()
plt.plot(zs,'o', alpha=0.5, marker='o', label='zs')
plt.plot(x[:,0], label='FLS')
plt.plot(xfl, label='FLS S')
plt.plot(xkf, label='KF')
plt.plot(rts_x[:,0], label='RTS')
plt.legend(loc=4)
plt.show()
print(fl_res)
print(kf_res)
print('std fixed lag:', np.mean(fl_res[N:]))
print('std kalman:', np.mean(kf_res[N:]))
return np.mean(fl_res) <= np.mean(kf_res)示例3: one_run_test_fls
# 需要导入模块: from filterpy.kalman import KalmanFilter [as 别名]
# 或者: from filterpy.kalman.KalmanFilter import rts_smoother [as 别名]
def one_run_test_fls():
fls = FixedLagSmoother(dim_x=2, dim_z=1)
fls.x = np.array([0., .5])
fls.F = np.array([[1.,1.],
[0.,1.]])
fls.H = np.array([[1.,0.]])
fls.P *= 200
fls.R *= 5.
fls.Q *= 0.001
kf = KalmanFilter(dim_x=2, dim_z=1)
kf.x = np.array([0., .5])
kf.F = np.array([[1.,1.],
[0.,1.]])
kf.H = np.array([[1.,0.]])
kf.P *= 2000
kf.R *= 1.
kf.Q *= 0.001
N = 4 # size of lag
nom = np.array([t/2. for t in range (0,40)])
zs = np.array([t + random.randn()*1.1 for t in nom])
xs, x = fls.smooth_batch(zs, N)
M,P,_,_ = kf.batch_filter(zs)
rts_x,_,_ = kf.rts_smoother(M, P)
xfl = xs[:,0].T[0]
xkf = M[:,0].T[0]
fl_res = abs(xfl-nom)
kf_res = abs(xkf-nom)
return np.mean(fl_res) <= np.mean(kf_res)示例4: range
# 需要导入模块: from filterpy.kalman import KalmanFilter [as 别名]
# 或者: from filterpy.kalman.KalmanFilter import rts_smoother [as 别名]
fk.F = np.array([[1.,1.],
[0.,1.]]) # state transition matrix
fk.H = np.array([[1.,0.]]) # Measurement function
fk.P = .01 # covariance matrix
fk.R = 5 # state uncertainty
fk.Q = 0.001 # process uncertainty
zs = [t + random.randn()*4 for t in range (40)]
mu, cov, _, _ = fk.batch_filter (zs)
mus = [x[0] for x in mu]
M,P,C = fk.rts_smoother(mu, cov)
# plot data
p1, = plt.plot(zs,'cyan', alpha=0.5)
p2, = plt.plot (M[:,0],c='b')
p3, = plt.plot (mus,c='r')
p4, = plt.plot ([0,len(zs)],[0,len(zs)], 'g') # perfect result
plt.legend([p1,p2, p3, p4],
["measurement", "RKS", "KF output", "ideal"], loc=4)
plt.show()