def phdUpdate(self, observation_set):
num_observations = observation_set.shape[0]
if num_observations:
z_dim = observation_set.shape[1]
else:
z_dim = 0
if not self.weights.shape[0]:
self._states_ = self.states.copy()
self._weights_ = self.weights.copy()
return
detection_probability = self.parameters.pd_fn.handle(self.states, self.parameters.pd_fn.parameters)
# clutter_pdf = [self.clutter_fn.handle(_observation_,
# self.clutter_fn.parameters) \
# for _observation_ in observation_set]
clutter_pdf = self.parameters.clutter_fn.handle(observation_set, self.parameters.clutter_fn.parameters)
# Account for missed detection
self._states_ = self.states.copy()
self._weights_ = [self.weights * (1 - detection_probability)]
# Split x and P out from the combined state vector
detected_indices = detection_probability > 0
detected_states = self.states[detected_indices]
x = detected_states.state
P = detected_states.covariance
# Scale the weights by detection probability
weights = self.weights[detected_indices] * detection_probability[detected_indices]
if x.shape[0]:
# Part of the Kalman update is common to all observation-updates
x, P, kalman_info = kalmanfilter.kf_update(
x,
P,
np.array([self.parameters.obs_fn.parameters.H]),
np.array([self.parameters.obs_fn.parameters.R]),
None,
INPLACE=True,
) # USE_NP=0)
# Container for the updated states
new_gmstate = self.states.__class__(0)
# We need to update the states and find the updated weights
for (_observation_, obs_count) in zip(observation_set, range(num_observations)):
# new_x = copy.deepcopy(x)
# Apply the Kalman update to get the new state - update in-place
# and return the residuals
new_x, residuals = kalmanfilter.kf_update_x(
x, kalman_info.pred_z, _observation_, kalman_info.kalman_gain, INPLACE=False
)
# Calculate the weight of the Gaussians for this observation
# Calculate term in the exponent
x_pdf = np.exp(-0.5 * np.power(blas.dgemv(kalman_info.inv_sqrt_S, residuals), 2).sum(axis=1)) / np.sqrt(
kalman_info.det_S * (2 * np.pi) ** z_dim
)
new_weight = weights * x_pdf
# Normalise the weights
normalisation_factor = clutter_pdf[obs_count] + new_weight.sum()
new_weight /= normalisation_factor
# Create new state with new_x and P to add to _states_
new_gmstate.set(new_x, P)
self._states_.append(new_gmstate)
self._weights_ += [new_weight]
self._weights_ = np.concatenate(self._weights_)
def phdUpdate(self, observation_set):
# Container for slam parent update
slam_info = PARAMETERS()
num_observations = observation_set.shape[0]
if num_observations:
z_dim = observation_set.shape[1]
else:
z_dim = 0
if not self.weights.shape[0]:
self._states_ = self.states.copy()
self._weights_ = self.weights.copy()
return
detection_probability = self.parameters.pd_fn.handle(self.states,
self.parameters.pd_fn.parameters)
#clutter_pdf = [self.clutter_fn.handle(_observation_,
# self.clutter_fn.parameters) \
# for _observation_ in observation_set]
clutter_pdf = self.parameters.clutter_fn.handle(observation_set,
self.parameters.clutter_fn.parameters)
# Account for missed detection
self._states_ = self.states.copy()
self._weights_ = [self.weights*(1-detection_probability)]
# SLAM, step 1:
slam_info.exp_sum__pd_predwt = np.exp(-self.weights.sum())
# Split x and P out from the combined state vector
detected_indices = detection_probability > 0.1
detected_states = self.states[detected_indices]
x = detected_states.state
P = detected_states.covariance
# Scale the weights by detection probability
weights = self.weights[detected_indices]*detection_probability[detected_indices]
# SLAM, prep for step 2:
slam_info.sum__clutter_with_pd_updwt = np.zeros(num_observations)
if x.shape[0]:
# Part of the Kalman update is common to all observation-updates
x, P, kalman_info = kf_update(x, P,
np.array([self.parameters.obs_fn.parameters.H]),
np.array([self.parameters.obs_fn.parameters.R]),
None, INPLACE=True)#USE_NP=0)
# Container for the updated states
new_gmstate = self.states.__class__(0)
# Predicted observation from the current states
pred_z = featuredetector.tf.relative(self.parameters.obs_fn.parameters.parent_state_xyz,
self.parameters.obs_fn.parameters.parent_state_rpy,
x)
#print "PREDICTED Z:"
#print pred_z
# We need to update the states and find the updated weights
for (_observation_, obs_count) in zip(observation_set,
range(num_observations)):
#new_x = copy.deepcopy(x)
# Apply the Kalman update to get the new state - update in-place
# and return the residuals
new_x, residuals = kf_update_x(x, pred_z,
_observation_, kalman_info.kalman_gain,
INPLACE=False)
code.interact(local=locals())
# Calculate the weight of the Gaussians for this observation
# Calculate term in the exponent
x_pdf = np.exp(-0.5*np.power(
blas.dgemv(kalman_info.inv_sqrt_S, residuals), 2).sum(axis=1))/ \
np.sqrt(kalman_info.det_S*(2*np.pi)**z_dim)
new_weight = weights*x_pdf
# Normalise the weights
normalisation_factor = clutter_pdf[obs_count] + new_weight.sum()
new_weight /= normalisation_factor
# SLAM, step 2:
slam_info.sum__clutter_with_pd_updwt[obs_count] = \
normalisation_factor
# Create new state with new_x and P to add to _states_
new_gmstate.set(new_x, P)
self._states_.append(new_gmstate)
self._weights_ += [new_weight]
else:
slam_info.sum__clutter_with_pd_updwt = np.array(clutter_pdf)
self._weights_ = np.concatenate(self._weights_)
# SLAM, finalise:
slam_info.likelihood = (slam_info.exp_sum__pd_predwt *
slam_info.sum__clutter_with_pd_updwt.prod())
return slam_info
