def test_1d_const_vel():
def hx(x):
return np.array([x[0]])
F = np.array([[1., 1.],[0., 1.]])
def fx(x, dt):
return np.dot(F, x)
x = np.array([0., 1.])
P = np.eye(2)* 100.
f = EnKF(x=x, P=P, dim_z=1, dt=1., N=8, hx=hx, fx=fx)
std_noise = 10.
f.R *= std_noise**2
f.Q = Q_discrete_white_noise(2, 1., .001)
measurements = []
results = []
ps = []
zs = []
s = Saver(f)
for t in range (0,100):
# create measurement = t plus white noise
z = t + randn()*std_noise
zs.append(z)
f.predict()
f.update(np.asarray([z]))
# save data
results.append (f.x[0])
measurements.append(z)
ps.append(3*(f.P[0,0]**.5))
s.save()
s.to_array()
results = np.asarray(results)
ps = np.asarray(ps)
if DO_PLOT:
plt.plot(results, label='EnKF')
plt.plot(measurements, c='r', label='z')
plt.plot (results-ps, c='k',linestyle='--', label='3$\sigma$')
plt.plot(results+ps, c='k', linestyle='--')
plt.legend(loc='best')
#print(ps)
return f
def test_1d_const_vel():
def hx(x):
return np.array([x[0]])
F = np.array([[1., 1.],[0., 1.]])
def fx(x, dt):
return np.dot(F, x)
x = np.array([0., 1.])
P = np.eye(2)* 100.
f = EnKF(x=x, P=P, dim_z=1, dt=1., N=8, hx=hx, fx=fx)
std_noise = 10.
f.R *= std_noise**2
f.Q = Q_discrete_white_noise(2, 1., .001)
measurements = []
results = []
ps = []
zs = []
for t in range (0,100):
# create measurement = t plus white noise
z = t + randn()*std_noise
zs.append(z)
f.predict()
f.update(np.asarray([z]))
# save data
results.append (f.x[0])
measurements.append(z)
ps.append(3*(f.P[0,0]**.5))
results = np.asarray(results)
ps = np.asarray(ps)
std_noise = 1
#初值
x = np.array([state[0]])
#状态协方差矩阵初始化
P = np.eye(1)
#ensemble kalman filter
enkf = EnKF(x=x, P=P, dim_z=1, dt=0.1, N=5, hx=hx, fx=fx)
#观测噪声
enkf.R *= std_noise**2
#系统噪声
enkf.Q = np.eye(1) * 1
filter_result = []
#方便写fx
global time
time = 0
#运行
for i in range(0, state.shape[0]):
m = measurements[i]
enkf.predict()
enkf.update(np.asarray([m]))
filter_result.append(enkf.x)
time = time + 1
filter_result = [x[0] for x in filter_result]
xxa = X.copy()
xa = np.array([xxa[:, i] for i in range(0, xxa.shape[1])]).flatten()
yya = Y.copy()
ya = np.array([yya[:, i] for i in range(0, yya.shape[1])]).flatten()
# Kalman filter
for i in range(1, npd):
F = np.array([[1., 0.], [0., 1.]])
zz = fval[:][i]
x = np.array([1., 0.])
P = np.eye(2) * 100.
enf = EnKF(x=x, P=P, dim_z=1, dt=1., N=5, hx=hx, fx=fx)
std_noise = 1e-6
enf.R *= std_noise**2
enf.Q = Q_discrete_white_noise(2, std_noise, std_noise)
results = np.zeros((1, tt)).T
for t in range(0, tt):
# create measurement = t plus white noise
z = zz[t] + randn() * std_noise
enf.predict()
enf.update(np.asarray([z]))
# save data
if zz[t] > 1e-12:
results[t] = enf.x[0]
else:
results[t] = zz[t]
results = results.flatten()
