)
# 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,
updater=updater,
)
# %%
# Create Metric Generators
# 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
# components.
from stonesoup.types.state import GaussianState
from stonesoup.initiator.simple import MultiMeasurementInitiator
initiator = MultiMeasurementInitiator(
prior_state=GaussianState([[0], [0], [0], [0]], np.diag([0, 1, 0, 1])),
measurement_model=measurement_model,
deleter=deleter,
data_associator=data_associator,
updater=updater,
min_points=2,
)
# %%
# Running the Tracker
# -------------------
# Loop through the predict, hypothesise, associate and update steps like before, but note on update
# which detections we've used at each time step. In each loop the deleter is called, returning
# tracks that are to be removed. Then the initiator is called with the unassociated detections, by
# removing the associated detections from the full set. The order of the deletion and initiation is
# important, so tracks that have just been created, aren't deleted straight away. (The
# implementation below is the same as :class:`~.MultiTargetTracker`)
tracks, all_tracks = set(), set()
# %%
# Track Initiation
# ****************
# For initialising tracks we will use a :class:`~.MultiMeasurementInitiator`, which allows our
# tracker to tentatively initiate tracks from unassociated measurements, and hold them within the
# initiator until they have survived for at least 10 frames. We also define a
# :class:`~.UpdateTimeStepsDeleter` deleter to be used by the initiator to delete tentative tracks
# that have not been associated to a measurement in the last 3 frames.
from stonesoup.types.state import GaussianState
from stonesoup.types.array import CovarianceMatrix, StateVector
from stonesoup.initiator.simple import MultiMeasurementInitiator
from stonesoup.deleter.time import UpdateTimeStepsDeleter
prior_state = GaussianState(StateVector(np.zeros((6,1))),
CovarianceMatrix(np.diag([100**2, 30**2, 100**2, 30**2, 100**2, 100**2])))
deleter_init = UpdateTimeStepsDeleter(time_steps_since_update=3)
initiator = MultiMeasurementInitiator(prior_state, measurement_model, deleter_init,
data_associator, updater, min_points=10)
# %%
# Track Deletion
# **************
# For confirmed tracks we used again a :class:`~.UpdateTimeStepsDeleter`, but this time configured
# to delete tracks after they have not bee associated to a measurement in the last 15 frames.
deleter = UpdateTimeStepsDeleter(time_steps_since_update=15)
# %%
# .. note::
#
# For more information on the above classes and how they operate you can refer to the Stone
# `Initiators & Deleters <https://stonesoup.readthedocs.io/en/latest/auto_tutorials/09_Initiators_&_Deleters.html>`_
# tutorial.
#
# Building the tracker
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,
updater=updater,
min_points=min_detections)
# %%
# Run the Tracker
# ---------------
# With the components created, the multi-target tracker component is created, constructed from
# the components specified above. This is logically the same as tracking code in the previous
# tutorial section :ref:`auto_tutorials/09_Initiators_&_Deleters:Running the Tracker`
from stonesoup.tracker.simple import MultiTargetTracker
tracker = MultiTargetTracker(
initiator=initiator,
deleter=deleter,
detector=detection_sim,
# The Initiator
# -------------
# We can now create the initiator. The :class:`~.MultiMeasurementInitiator` will initiate
# and hold tracks until enough detections have been associated with the track. It will then
# proceed to release the tracks to the tracker.
from stonesoup.initiator.simple import MultiMeasurementInitiator
from stonesoup.types.state import GaussianState
initiator = MultiMeasurementInitiator(
GaussianState(
np.array([[0], [0], [0], [0], [0], [0]]), # Prior State
np.diag([15**2, 100**2, 15**2, 100**2, 15**2, 20**2])),
measurement_model=None,
deleter=deleter,
data_associator=GNNWith2DAssignment(
DistanceHypothesiser(init_predictor,
updater,
Mahalanobis(),
missed_distance=3)),
updater=updater,
min_points=2)
# %%
# The Tracker
# -----------
# Next, we bring together all the components we’ve assembled to construct our :class:`~.MultiTargetTracker`.
# A loop is created to generate tracks at each time interval, and store them in a set called tracks.
from stonesoup.tracker.simple import MultiTargetTracker