This example shows how to animate several state sequences to be plotted in time order.
Building a Simple Simulation and Tracker
For simplicity, we are going to quickly build a basic Kalman Tracker, with simple Stone Soup simulators, including clutter. In this case a 2D constant velocity target, with 2D linear measurements of position.
All non-generic imports will be given in order of usage.
import datetime import numpy as np from stonesoup.dataassociator.neighbour import GNNWith2DAssignment from stonesoup.deleter.error import CovarianceBasedDeleter from stonesoup.hypothesiser.distance import DistanceHypothesiser from stonesoup.initiator.simple import MultiMeasurementInitiator from stonesoup.measures import Mahalanobis from stonesoup.models.transition.linear import ( CombinedLinearGaussianTransitionModel, ConstantVelocity) from stonesoup.models.measurement.linear import LinearGaussian from stonesoup.predictor.kalman import KalmanPredictor from stonesoup.simulator.simple import MultiTargetGroundTruthSimulator, SimpleDetectionSimulator from stonesoup.tracker.simple import MultiTargetTracker from stonesoup.types.array import StateVector, CovarianceMatrix from stonesoup.types.state import GaussianState from stonesoup.updater.kalman import KalmanUpdater
Set up the platform and detection simulators
# Models transition_model = CombinedLinearGaussianTransitionModel( [ConstantVelocity(1), ConstantVelocity(1)]) measurement_model = LinearGaussian(4, [0, 2], np.diag([0.5, 0.5])) start_time = datetime.datetime.now() timestep = datetime.timedelta(seconds=5) # Simulators groundtruth_sim = MultiTargetGroundTruthSimulator( transition_model=transition_model, initial_state=GaussianState( StateVector([, , , ]), CovarianceMatrix(np.diag([1000, 10, 1000, 10])), timestamp=start_time), timestep=timestep, number_steps=60, birth_rate=0.15, death_probability=0.05 ) 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, )
Set up the tracker
# 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( prior_state=GaussianState(np.array([, , , ]), np.diag([0, 100, 0, 1000]), timestamp=start_time), measurement_model=measurement_model, deleter=deleter, data_associator=data_associator, updater=updater, min_points=3, ) # Tracker tracker = MultiTargetTracker( initiator=initiator, deleter=deleter, detector=detection_sim, data_associator=data_associator, updater=updater, )
Run the simulation
from stonesoup.types.detection import Clutter, TrueDetection average_life_of_gt = timestep * sum(len(gt) for gt in groundtruth)/len(groundtruth) n_clutter = sum(isinstance(det, Clutter) for det in detections) n_true_detections = sum(isinstance(det, TrueDetection) for det in detections) average_life_of_track = timestep * sum(len(track) for track in all_tracks)/len(all_tracks) print("The simulation produced:\n", len(groundtruth), "Ground truth paths with an average life of", average_life_of_gt, "\n", n_clutter, "Clutter Detections\n", n_true_detections, "True Detections\n", len(all_tracks), "Tracks with an average life of", average_life_of_track, "\n", )
The simulation produced: 9 Ground truth paths with an average life of 0:01:02.777778 51 Clutter Detections 102 True Detections 9 Tracks with an average life of 0:01:08.888889
Create the Animation
from stonesoup.plotter import AnimationPlotter
Create the plotter object and use the plot_# functions to assign the data to plot
Run the Animation
The following is needed to ensure that the animation is made playable via interactive player in Jupyter Notebooks.
import matplotlib matplotlib.rcParams['animation.html'] = 'jshtml'
To avoid the figure becoming too cluttered with old information, states older than 60 seconds will not be shown.