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Fix process noise handling in UKF #279

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Fix process noise handling in UKF #279

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13 changes: 10 additions & 3 deletions filterpy/kalman/UKF.py
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Original file line number Diff line number Diff line change
Expand Up @@ -361,7 +361,7 @@ def __init__(self, dim_x, dim_z, dt, hx, fx, points,
self.x_post = self.x.copy()
self.P_post = self.P.copy()

def predict(self, dt=None, UT=None, fx=None, **fx_args):
def predict(self, dt=None, Q=None, UT=None, fx=None, **fx_args):
r"""
Performs the predict step of the UKF. On return, self.x and
self.P contain the predicted state (x) and covariance (P). '
Expand All @@ -376,6 +376,10 @@ def predict(self, dt=None, UT=None, fx=None, **fx_args):
If specified, the time step to be used for this prediction.
self._dt is used if this is not provided.

Q : numpy.array((dim_x, dim_x)), optional
Process noise. If provided, overrides self.Q for
this function call.

fx : callable f(x, dt, **fx_args), optional
State transition function. If not provided, the default
function passed in during construction will be used.
Expand All @@ -393,14 +397,17 @@ def predict(self, dt=None, UT=None, fx=None, **fx_args):
if dt is None:
dt = self._dt

if Q is None:
Q = self.Q

if UT is None:
UT = unscented_transform

# calculate sigma points for given mean and covariance
self.compute_process_sigmas(dt, fx, **fx_args)

#and pass sigmas through the unscented transform to compute prior
self.x, self.P = UT(self.sigmas_f, self.Wm, self.Wc, self.Q,
self.x, self.P = UT(self.sigmas_f, self.Wm, self.Wc, Q,
self.x_mean, self.residual_x)

# update sigma points to reflect the new variance of the points
Expand Down Expand Up @@ -718,7 +725,7 @@ def rts_smoother(self, Xs, Ps, Qs=None, dts=None, UT=None):
sigmas_f[i] = self.fx(sigmas[i], dts[k])

xb, Pb = UT(
sigmas_f, self.Wm, self.Wc, self.Q,
sigmas_f, self.Wm, self.Wc, Qs[k],
self.x_mean, self.residual_x)

# compute cross variance
Expand Down