num_estimates = timesteps + max(ais_meas_rate, radar_meas_rate)
generate_scenario_3(seed=seed,
permanent_save=False,
radar_meas_rate=radar_meas_rate,
ais_meas_rate=ais_meas_rate,
sigma_process=sigma_process,
sigma_meas_radar=sigma_meas_radar,
sigma_meas_ais=sigma_meas_ais,
timesteps=timesteps)
folder = "temp" # temp instead of seed, as it is not a permanent save
# load ground truth and the measurements
data_folder = "../scenarios/scenario3/" + folder + "/"
ground_truth = open_object.open_object(data_folder + "ground_truth.pk1")
measurements_radar = open_object.open_object(data_folder +
"measurements_radar.pk1")
measurements_ais = open_object.open_object(data_folder +
"measurements_ais.pk1")
# load start_time
start_time = open_object.open_object(data_folder + "start_time.pk1")
# prior
prior = GaussianState([0, 1, 0, 1],
np.diag([1.5, 0.5, 1.5, 0.5])**2,
timestamp=start_time)
kf_independent_fusion = kalman_filter_independent_fusion(
start_time,
from stonesoup.models.transition.linear import CombinedLinearGaussianTransitionModel, ConstantVelocity
from stonesoup.predictor.kalman import KalmanPredictor
from stonesoup.types.state import GaussianState
from stonesoup.updater.kalman import KalmanUpdater
from stonesoup.types.hypothesis import SingleHypothesis
from stonesoup.types.track import Track
from utils import open_object
from data_fusion import track_to_track_association
from data_fusion import track_to_track_fusion
from trackers.calc_cross_cov_estimate_error import calc_cross_cov_estimate_error
# load ground truth and the measurements
ground_truth = open_object.open_object(
"../scenarios/scenario2/ground_truth.pk1")
measurements_radar = open_object.open_object(
"../scenarios/scenario2/measurements_radar.pk1")
measurements_ais = open_object.open_object(
"../scenarios/scenario2/measurements_ais.pk1")
# load start_time
start_time = open_object.open_object("../scenarios/scenario2/start_time.pk1")
# same transition models (radar uses same as original)
transition_model_radar = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(0.01), ConstantVelocity(0.01)])
transition_model_ais = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(0.01), ConstantVelocity(0.01)])
# same measurement models as used when generating the measurements