meas_range=np.array([[-1, 1], [-1, 1]]) * 5000, # Area to generate clutter
detection_probability=0.9,
clutter_rate=1,
seed=4
)
# Filter
predictor = KalmanPredictor(transition_model)
updater = KalmanUpdater(measurement_model)
# Data Associator
hypothesiser = DistanceHypothesiser(predictor, updater, Mahalanobis(), missed_distance=3)
data_associator = GNNWith2DAssignment(hypothesiser)
# Initiator & Deleter
deleter = CovarianceBasedDeleter(covar_trace_thresh=1E3)
initiator = MultiMeasurementInitiator(
GaussianState(np.array([[0], [0], [0], [0]]), np.diag([0, 100, 0, 1000])),
measurement_model=measurement_model,
deleter=deleter,
data_associator=data_associator,
updater=updater,
min_points=3,
)
# Tracker
tracker = MultiTargetTracker(
initiator=initiator,
deleter=deleter,
detector=detection_sim,
data_associator=data_associator,
measure=Mahalanobis(),
missed_distance=3)
from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
data_associator = GNNWith2DAssignment(hypothesiser)
# %%
# Creating a Deleter
# ------------------
# Here we are going to create an error based deleter, which will delete any :class:`~.Track` where
# trace of the covariance is over a certain threshold, i.e. when we have a high uncertainty. This
# simply requires a threshold to be defined, which will depend on units and number of dimensions of
# your state vector. So the higher the threshold value, the longer tracks that haven't been
# updated will remain.
from stonesoup.deleter.error import CovarianceBasedDeleter
deleter = CovarianceBasedDeleter(covar_trace_thresh=4)
# %%
# Creating an Initiator
# ---------------------
# Here we are going to use a measurement based initiator, which will create a track from the
# unassociated :class:`~.Detection` objects. A prior needs to be defined for the entire state
# but elements of the state that are measured are replaced by state of the measurement, including
# the measurement's uncertainty (noise covariance defined by the :class:`~.MeasurementModel`). In
# this example, as our sensor measures position (as defined in measurement model
# :attr:`~.LinearGaussian.mapping` attribute earlier), we only need to modify the values for the
# velocity and its variance.
#
# As we are dealing with clutter, here we are going to be using a multi-measurement initiator. This
# requires that multiple measurements are added to a track before being initiated. In this example,
# this initiator effectively runs a mini version of the same tracker, but you could use different
updater,
measure=Mahalanobis(),
missed_distance=3)
# %%
# Initialise the GNN with the hypothesiser.
from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
data_associator = GNNWith2DAssignment(hypothesiser)
# %%
# Initiator and Deleter
# ^^^^^^^^^^^^^^^^^^^^^
# Create deleter - get rid of anything with a covariance trace greater than 2
from stonesoup.deleter.error import CovarianceBasedDeleter
covariance_limit_for_delete = 2
deleter = CovarianceBasedDeleter(covariance_limit_for_delete)
# %%
# Set a standard prior state and the minimum number of detections required to qualify for
# initiation
s_prior_state = GaussianState([[0], [0], [0], [0]], np.diag([0, 0.5, 0, 0.5]))
min_detections = 3
# %%
# Initialise the initiator - use the 'full tracker' components specified above in the initiator.
# But note that other ones could be used if needed.
from stonesoup.initiator.simple import MultiMeasurementInitiator
initiator = MultiMeasurementInitiator(prior_state=s_prior_state,
measurement_model=measurement_model,
deleter=deleter,
data_associator=data_associator,
updater,
measure=Mahalanobis(),
missed_distance=3)
# %%
# Initialise the GNN with the hypothesiser.
from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
data_associator = GNNWith2DAssignment(hypothesiser)
# %%
# Initiator and Deleter
# ^^^^^^^^^^^^^^^^^^^^^
# Create deleter - get rid of anything with a covariance trace greater than 2
from stonesoup.deleter.error import CovarianceBasedDeleter
covariance_limit_for_delete = 2
deleter = CovarianceBasedDeleter(
covar_trace_thresh=covariance_limit_for_delete)
# %%
# Set a standard prior state and the minimum number of detections required to qualify for
# initiation
s_prior_state = GaussianState([[0], [0], [0], [0]], np.diag([0, 0.5, 0, 0.5]))
min_detections = 3
# %%
# Initialise the initiator - use the 'full tracker' components specified above in the initiator.
# But note that other ones could be used if needed.
from stonesoup.initiator.simple import MultiMeasurementInitiator
initiator = MultiMeasurementInitiator(prior_state=s_prior_state,
measurement_model=measurement_model,
deleter=deleter,
data_associator=data_associator,
measure=Mahalanobis(),
missed_distance=3)
from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
data_associator = GNNWith2DAssignment(hypothesiser)
# %%
# Creating a Deleter
# ------------------
# Here we are going to create an error based deleter, which will delete any :class:`~.Track` where
# trace of the covariance is over a certain threshold, i.e. when we have a high uncertainty. This
# simply requires a threshold to be defined, which will depend on units and number of dimensions of
# your state vector. So the higher the threshold value, the longer tracks that haven't been
# updated will remain.
from stonesoup.deleter.error import CovarianceBasedDeleter
deleter = CovarianceBasedDeleter(4)
# %%
# Creating an Initiator
# ---------------------
# Here we are going to use a measurement based initiator, which will create a track from the
# unassociated :class:`~.Detection` objects. A prior needs to be defined for the entire state
# but elements of the state that are measured are replaced by state of the measurement, including
# the measurement's uncertainty (noise covariance defined by the :class:`~.MeasurementModel`). In
# this example, as our sensor measures position (as defined in measurement model
# :attr:`~.LinearGaussian.mapping` attribute earlier), we only need to modify the values for the
# velocity and its variance.
#
# As we are dealing with clutter, here we are going to be using a multi-measurement initiator. This
# requires that multiple measurements are added to a track before being initiated. In this example,
# this initiator effectively runs a mini version of the same tracker, but you could use different