)
detection_sim = SimpleDetectionSimulator(
groundtruth=groundtruth_sim,
measurement_model=measurement_model,
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(
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
# Now create a resampler and particle updater
resampler = SystematicResampler()
updater = ParticleUpdater(measurement_model=None,
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
)
# %%
# tutorials. The metric for this is the Mahalanobis distance. We also require two
# objects which together form the predictor and to generate hypotheses (or predictions)
# based on the previous state. Recall that the GM-PHD propagates the first-order
# statistical moment which is a Gaussian mixture. The predicted state at the next time
# step is also a Gaussian mixture and can be determined solely by the propagated prior.
# Determining this predicted Gaussian mixture is the job for the
# :class:`~.GaussianMixtureHypothesiser` class. We must also generate a prediction for each track
# in the simulation, and so use the :class:`~.DistanceHypothesiser` object as before.
from stonesoup.predictor.kalman import KalmanPredictor
kalman_predictor = KalmanPredictor(transition_model)
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
base_hypothesiser = DistanceHypothesiser(kalman_predictor,
kalman_updater,
Mahalanobis(),
missed_distance=3)
from stonesoup.hypothesiser.gaussianmixture import GaussianMixtureHypothesiser
hypothesiser = GaussianMixtureHypothesiser(base_hypothesiser,
order_by_detection=True)
# %%
# The updater takes a list of hypotheses from the hypothesiser and transforms them into
# potential new states for our tracks. Each state is a :class:`~.TaggedWeightedGaussianState`
# object and has a state vector, covariance, weight, tag, and timestamp. Some of the
# updated states have a very low weight, indicating that they do not contribute much to
# the Gaussian mixture. To ease the computational complexity, a :class:`~.GaussianMixtureReducer`
# is used to merge and prune many of the states based on provided thresholds. States whose
# distance is less than the merging threshold will be combined, and states whose weight
# is less than the pruning threshold will be removed.
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 # ****************
