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(
initiator=initiator,
from stonesoup.predictor.kalman import ExtendedKalmanPredictor
predictor = ExtendedKalmanPredictor(transition_model)
init_predictor = ExtendedKalmanPredictor(init_transition_model)
from stonesoup.updater.kalman import ExtendedKalmanUpdater
updater = ExtendedKalmanUpdater(measurement_model=None)
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
hypothesiser = DistanceHypothesiser(predictor,
updater,
Mahalanobis(),
missed_distance=5)
from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
data_associator = GNNWith2DAssignment(hypothesiser)
from stonesoup.deleter.time import UpdateTimeDeleter
deleter = UpdateTimeDeleter(datetime.timedelta(seconds=20),
delete_last_pred=True)
# %%
# 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