def test_noisy_1d():
f = FadingKalmanFilter(3., dim_x=2, dim_z=1)
f.x = np.array([[2.], [0.]]) # initial state (location and velocity)
f.F = np.array([[1., 1.], [0., 1.]]) # state transition matrix
f.H = np.array([[1., 0.]]) # Measurement function
f.P *= 1000. # covariance matrix
f.R = 5.**2 # state uncertainty
f.Q = np.array([[0, 0], [0, 0.0001]]) # process uncertainty
measurements = []
results = []
zs = []
for t in range(100):
# create measurement = t plus white noise
z = t + random.randn() * np.sqrt(f.R)
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
# save data
results.append(f.x[0, 0])
measurements.append(z)
# test mahalanobis
a = np.zeros(f.y.shape)
maha = scipy_mahalanobis(a, f.y, f.SI)
assert f.mahalanobis == approx(maha)
print(z, maha, f.y, f.S)
assert maha < 4
# now do a batch run with the stored z values so we can test that
# it is working the same as the recursive implementation.
# give slightly different P so result is slightly different
f.X = np.array([[2., 0]]).T
f.P = np.eye(2) * 100.
m, c, _, _ = f.batch_filter(zs, update_first=False)
# plot data
if DO_PLOT:
p1, = plt.plot(measurements, 'r', alpha=0.5)
p2, = plt.plot(results, 'b')
p4, = plt.plot(m[:, 0], 'm')
p3, = plt.plot([0, 100], [0, 100], 'g') # perfect result
plt.legend([p1, p2, p3, p4],
["noisy measurement", "KF output", "ideal", "batch"],
loc=4)
plt.show()
def test_noisy_1d():
f = FadingKalmanFilter (5., dim_x=2, dim_z=1)
f.X = np.array([[2.],
[0.]]) # initial state (location and velocity)
f.F = np.array([[1.,1.],
[0.,1.]]) # state transition matrix
f.H = np.array([[1.,0.]]) # Measurement function
f.P *= 1000. # covariance matrix
f.R = 5 # state uncertainty
f.Q = 0.0001 # process uncertainty
measurements = []
results = []
zs = []
for t in range (100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
# save data
results.append (f.X[0,0])
measurements.append(z)
# now do a batch run with the stored z values so we can test that
# it is working the same as the recursive implementation.
# give slightly different P so result is slightly different
f.X = np.array([[2.,0]]).T
f.P = np.eye(2)*100.
m,c,_,_ = f.batch_filter(zs,update_first=False)
# plot data
if DO_PLOT:
p1, = plt.plot(measurements,'r', alpha=0.5)
p2, = plt.plot (results,'b')
p4, = plt.plot(m[:,0], 'm')
p3, = plt.plot ([0,100],[0,100], 'g') # perfect result
plt.legend([p1,p2, p3, p4],
["noisy measurement", "KF output", "ideal", "batch"], loc=4)
plt.show()
def test_noisy_1d():
f = FadingKalmanFilter (5., dim_x=2, dim_z=1)
f.X = np.array([[2.],
[0.]]) # initial state (location and velocity)
f.F = np.array([[1.,1.],
[0.,1.]]) # state transition matrix
f.H = np.array([[1.,0.]]) # Measurement function
f.P *= 1000. # covariance matrix
f.R = 5 # state uncertainty
f.Q = 0.0001 # process uncertainty
measurements = []
results = []
zs = []
for t in range (100):
# create measurement = t plus white noise
z = t + random.randn()*20
zs.append(z)
# perform kalman filtering
f.update(z)
f.predict()
# save data
results.append (f.X[0,0])
measurements.append(z)
# now do a batch run with the stored z values so we can test that
# it is working the same as the recursive implementation.
# give slightly different P so result is slightly different
f.X = np.array([[2.,0]]).T
f.P = np.eye(2)*100.
m,c,_,_ = f.batch_filter(zs,update_first=False)
