resampler=resampler)
# Create a particle initiator
from stonesoup.initiator.simple import GaussianParticleInitiator, SinglePointInitiator
single_point_initiator = SinglePointInitiator(
GaussianState([[0], [-40], [2000], [0], [8000], [0]], np.diag([10000, 1000, 10000, 1000, 10000, 1000])),
None)
initiator = GaussianParticleInitiator(number_particles=500,
initiator=single_point_initiator)
hypothesiser = DistanceHypothesiser(predictor, updater, measure=Mahalanobis(), missed_distance=np.inf)
data_associator = GNNWith2DAssignment(hypothesiser)
from stonesoup.deleter.time import UpdateTimeStepsDeleter
deleter = UpdateTimeStepsDeleter(time_steps_since_update=10)
# Create a Kalman single-target tracker
tracker = SingleTargetTracker(
initiator=initiator,
deleter=deleter,
detector=sim,
data_associator=data_associator,
updater=updater
)
# %%
# The final step is to iterate our tracker over the simulation and plot out the results. Because we have a bearing
# only sensor it does not make sense to plot out the detections without animating the resulting plot. This
# animation shows the sensor platform (blue) moving towards the true target position (red). The estimated target
# position is shown in black, radar detections are shown in yellow while the bearing only imager detections are
# %%
# 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.