from stonesoup.updater.kalman import KalmanUpdater updater = KalmanUpdater(measurement_model) #%% # .. note:: # # For more information on the above classes and how they operate you can refer to the Stone # Soup tutorial on # `using the Kalman Filter <https://stonesoup.readthedocs.io/en/latest/auto_tutorials/01_KalmanFilterTutorial.html>`_. # # Data Association # **************** # We utilise a :class:`~.DistanceHypothesiser` to generate hypotheses between tracks and # measurements, where :class:`~.Mahalanobis` distance is used as a measure of quality: from stonesoup.hypothesiser.distance import DistanceHypothesiser from stonesoup.measures import Mahalanobis hypothesiser = DistanceHypothesiser(predictor, updater, Mahalanobis(), 10) # %% # Continuing the :class:`~.GNNWith2DAssigment` class is used to perform fast joint data association, # based on the Global Nearest Neighbour (GNN) algorithm: from stonesoup.dataassociator.neighbour import GNNWith2DAssignment data_associator = GNNWith2DAssignment(hypothesiser) #%% # .. note:: # For more information on the above classes and how they operate you can refer to the # `Data Association - clutter <https://stonesoup.readthedocs.io/en/latest/auto_tutorials/05_DataAssociation-Clutter.html>`_ # and `Data Association - Multi-Target Tracking <https://stonesoup.readthedocs.io/en/latest/auto_tutorials/06_DataAssociation-MultiTargetTutorial.html>`_ # tutorials. # %% # Track Initiation # ****************
return set()
deleter = MyDeleter()
# %%
# Setup Hypothesiser and Associator
# ---------------------------------
# Since we know there is only one measurement per scan, we can just use the
# :class:`~.NearestNeighbour` associator to achieve our desired result.
from stonesoup.measures import Euclidean
from stonesoup.dataassociator.neighbour import NearestNeighbour
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.tracker.simple import SingleTargetTracker
meas = Euclidean()
hypothesiser = DistanceHypothesiser(predictor, updater, meas)
associator = NearestNeighbour(hypothesiser)
tracker = SingleTargetTracker(initiator, deleter, detector, associator,
updater)
# %%
# Run the Tracker
# ---------------------------------
# We extract the ground truth from the detector and then run the tracker.
# While running the tracker we:
#
# - Extract the measurement that is associated with it.
# - Extract the position components of the estimated state vector.
#
# This allows us to plot the measurements, ground truth, and state estimates.
# %%
# Creating a Tracker
# ------------------
# We'll now create the tracker components as we did with the multi-target examples previously.
from stonesoup.predictor.kalman import KalmanPredictor
predictor = KalmanPredictor(transition_model)
from stonesoup.updater.kalman import KalmanUpdater
updater = KalmanUpdater(measurement_model)
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
hypothesiser = DistanceHypothesiser(predictor,
updater,
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)
updater = KalmanUpdater(measurement_model)
# %%
# Creating data associators
# -------------------------
# To associate our detections to track objects generate hypotheses using a hypothesiser. In this
# case we are using a :class:`~.Mahalanobis` distance measure. In addition, we are exploiting the
# fact that the detections should have the same |MMSI|_ for a single vessel, by "gating" out
# detections that don't match the tracks |MMSI|_ (this being populated by detections used to create
# the track).
from stonesoup.gater.filtered import FilteredDetectionsGater
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
measure = Mahalanobis()
hypothesiser = FilteredDetectionsGater(DistanceHypothesiser(predictor,
updater,
measure,
missed_distance=3),
metadata_filter="MMSI")
# %%
# We will use a nearest-neighbour association algorithm, passing in the Mahalanobis distance
# hypothesiser built in the previous step.
from stonesoup.dataassociator.neighbour import NearestNeighbour
data_associator = NearestNeighbour(hypothesiser)
# %%
# Creating track initiators and deleters
# --------------------------------------
# We need a method to initiate tracks. For this we will create an initiator component
# and have it generate a :class:`~.GaussianState`. In this case, we'll use a measurement initiator
# which uses the measurements value and model covariance where possible. In this case, as we are
