Multi_Tracker_Example.py

#!/usr/bin/env python

"""
Comparing Multiple Trackers On Manoeuvring Targets
==================================================
"""
# %%
# This example shows how multiple trackers can be compared against each other using the same
# set of detections.

# %%
# This notebook creates groundtruth with manoeuvring motion, generates detections using a sensor,
# and attempts to track the groundtruth using the
# Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF),
# the Particle Filter (PF), and the Extended Sliding Innovation Filter (ESIF).
# Each of these trackers assumes a constant velocity transition model. The trackers are compared
# against each other using distance-error metrics, with the capability of displaying other metrics
# for the user to explore. The aim of this example is to display the effectiveness of
# different trackers when tasked with following a manoeuvring target with non-linear detections.

# %%
# Layout
# ^^^^^^
# The layout of this example is as follows:
# 
# 1) The ground truth is created using multiple transition models
# 2) The non-linear detections are generated once per time step using a bearing-range sensor
# 3) Each tracker is initialised and run on the detections
# 4) The results are plotted, and tracking metrics displayed for the user to explore

# %%
# 1) Create Groundtruth
# ^^^^^^^^^^^^^^^^^^^^^
# Firstly, we initialise the ground truth states:
import numpy as np
import datetime
from stonesoup.types.array import StateVector, CovarianceMatrix
from stonesoup.types.state import State, GaussianState

start_time = datetime.datetime.now()
np.random.seed(2)
initial_state_mean = StateVector([[0], [0], [0], [0]])
initial_state_covariance = CovarianceMatrix(np.diag([4, 0.5, 4, 0.5]))
timestep_size = datetime.timedelta(seconds=1)
number_steps = 20
initial_state = GaussianState(initial_state_mean, initial_state_covariance)

# %%
# Next, we initialise the transition models used to generate the ground truth. Here, we say that
# the targets will mostly go straight ahead with a constant velocity, but will sometimes turn
# left or right. This is implemented using the :class:`~.SwitchMultiTargetGroundTruthSimulator`.
from stonesoup.models.transition.linear import (
    CombinedLinearGaussianTransitionModel, ConstantVelocity, KnownTurnRate)

# initialise the transition models the ground truth can use
constant_velocity = CombinedLinearGaussianTransitionModel(
    [ConstantVelocity(0.05), ConstantVelocity(0.05)])
turn_left = KnownTurnRate([0.05, 0.05], np.radians(20))
turn_right = KnownTurnRate([0.05, 0.05], np.radians(-20))

# create a probability matrix - how likely the ground truth will use each transition model,
# given its current model
model_probs = np.array([[0.7, 0.15, 0.15],  # keep straight, turn left, turn right
                        [0.4, 0.6, 0.0],  # go straight, keep turning left, turn right
                        [0.4, 0.0, 0.6]])  # go straight, turn left, keep turning right

# %%
from stonesoup.simulator.simple import SwitchMultiTargetGroundTruthSimulator
from stonesoup.types.state import GaussianState
# generate truths

n_truths = 3
xmin = 0
xmax = 40
ymin = 0
ymax = 40
preexisting_states = []

for i in range(0, n_truths):
    x = np.random.randint(xmin, xmax) - 1  # x position of initial state
    y = np.random.randint(ymin, ymax) - 1  # y position of initial state
    y_vel = np.random.randint(-20, 20) / 10  # x velocity will start between -2 and 2
    x_vel = np.random.randint(-20, 20) / 10  # y velocity will start between -2 and 2
    preexisting_states.append(StateVector([x, x_vel, y, y_vel]))

# %%
# Now we have initialised everything, so we can generate the ground truth:
ground_truth_gen = SwitchMultiTargetGroundTruthSimulator(
    initial_state=initial_state,
    transition_models=[constant_velocity, turn_left, turn_right],
    model_probs=model_probs,  # put in matrix from above
    number_steps=number_steps,  # how long we want each track to be
    timestep=timestep_size,
    birth_rate=0,
    death_probability=0,
    preexisting_states=preexisting_states
)

# %%
# This has created ground truth that has some twists and turns in it, which we will use to
# generate detections.

# %%
# 2) Generate detections using a bearing-range sensor
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# The next step is to create a sensor and use it to generate detections from the targets.
# The sensor we use in this example is a radar with imperfect measurements in bearing-range space.
# 
# First we initialise the radar:
from stonesoup.sensor.radar import RadarBearingRange

# Create the sensor
sensor = RadarBearingRange(
    ndim_state=4,
    position_mapping=[0, 2],  # Detecting x and y
    noise_covar=np.diag([np.radians(0.2), 0.2]),  # Radar doesn't take perfect measurements
    clutter_model=None,  # Can add clutter model in future if desired
)

# %%
# Now we place the sensor into the simulation:

from stonesoup.platform import FixedPlatform
platform = FixedPlatform(State(StateVector([20, 0, 0, 0])), position_mapping=[0, 2],
                         sensors=[sensor])

# %%
# Now we run the sensor and create detections:

from itertools import tee
from stonesoup.simulator.platform import PlatformDetectionSimulator

detector = PlatformDetectionSimulator(ground_truth_gen, platforms=[platform])
detector, *detectors = tee(detector, 6)
# Enables multiple trackers to run on the same detections

# %%
# We put the detections and ground truths into sets so that we can plot them:
detections = set()
ground_truth = set()

for time, dets in detector:
    detections |= dets
    ground_truth |= ground_truth_gen.groundtruth_paths

# %%
# And now we plot the ground truth and detections:

from stonesoup.plotter import Plotterly

plotter = Plotterly()
plotter.plot_ground_truths(ground_truth, [0, 2])
plotter.plot_measurements(detections, [0, 2])
plotter.plot_sensors(sensor)
plotter.fig

# %%
# 3) Initialise and run each tracker on the detections
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# With the detections now generated, our focus turns to creating and running the trackers.
# This section of the notebook is quite long because each tracker requires an initiator, deleter,
# detector, data associator, and updater. However, all of these things are standard
# stonesoup building blocks.
#
# Firstly, we approximate the transition model of the target. Here we assume a constant
# velocity model,
# which will be wrong due to the fact that we designed the targets to sometimes turn left or right.
# We do this to test how effectively each tracking algorithm can perform against
# target behaviour that doesn't move exactly as predicted.

transition_model_estimate = CombinedLinearGaussianTransitionModel([ConstantVelocity(0.5),
                                                                   ConstantVelocity(0.5)])
# Tracking algorithm incorrectly estimates the type of path the truth takes

# %%
# Next, we initialise the predictors, updaters, hypothesisers, data associators, and deleter.
# The particle filter requires a resampler as part of its updater.
# Note that the ESIF is a slight extension of the EKF and uses an EKF predictor.

from stonesoup.predictor.kalman import ExtendedKalmanPredictor, UnscentedKalmanPredictor
from stonesoup.predictor.particle import ParticlePredictor
# introduce the predictors
predictor_EKF = ExtendedKalmanPredictor(transition_model_estimate)
predictor_UKF = UnscentedKalmanPredictor(transition_model_estimate)
predictor_PF = ParticlePredictor(transition_model_estimate)

# ######################################################################

from stonesoup.resampler.particle import ESSResampler
resampler = ESSResampler()

# ######################################################################

from stonesoup.updater.kalman import ExtendedKalmanUpdater, UnscentedKalmanUpdater
from stonesoup.updater.slidinginnovation import ExtendedSlidingInnovationUpdater
from stonesoup.updater.particle import ParticleUpdater
# introduce the updaters

updater_EKF = ExtendedKalmanUpdater(sensor)
updater_UKF = UnscentedKalmanUpdater(sensor)
updater_ESIF = ExtendedSlidingInnovationUpdater(measurement_model=None,
                                                layer_width=10 * np.diag(sensor.noise_covar))
updater_PF = ParticleUpdater(measurement_model=None, resampler=resampler)

# ######################################################################

from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
# introduce the hypothesisers

hypothesiser_EKF = DistanceHypothesiser(predictor_EKF, updater_EKF,
                                        measure=Mahalanobis(), missed_distance=4)
hypothesiser_UKF = DistanceHypothesiser(predictor_UKF, updater_UKF,
                                        measure=Mahalanobis(), missed_distance=4)
hypothesiser_ESIF = DistanceHypothesiser(predictor_EKF, updater_ESIF,
                                         measure=Mahalanobis(), missed_distance=4)
hypothesiser_PF = DistanceHypothesiser(predictor_PF, updater_PF,
                                      measure=Mahalanobis(), missed_distance=4)

# ######################################################################

from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
# introduce the data associators

data_associator_EKF = GNNWith2DAssignment(hypothesiser_EKF)
data_associator_UKF = GNNWith2DAssignment(hypothesiser_UKF)
data_associator_ESIF = GNNWith2DAssignment(hypothesiser_ESIF)
data_associator_PF = GNNWith2DAssignment(hypothesiser_PF)

# ######################################################################

from stonesoup.deleter.time import UpdateTimeDeleter
# create a deleter
deleter = UpdateTimeDeleter(datetime.timedelta(seconds=5), delete_last_pred=True)

# %%
# Now we introduce the initial predictors which will be used in the data associator
# in the track initiators:
init_transition_model = CombinedLinearGaussianTransitionModel(
    (ConstantVelocity(1), ConstantVelocity(1)))
init_predictor_EKF = ExtendedKalmanPredictor(init_transition_model)
init_predictor_UKF = UnscentedKalmanPredictor(init_transition_model)
init_predictor_PF = ParticlePredictor(init_transition_model)

# %%
# The final step before running the trackers is to create the initiators:
from stonesoup.initiator.simple import MultiMeasurementInitiator

initiator_EKF = MultiMeasurementInitiator(
    GaussianState(
        np.array([[20], [0], [10], [0]]),  # Prior State
        np.diag([1, 1, 1, 1])),
    measurement_model=None,
    deleter=deleter,
    data_associator=GNNWith2DAssignment(
        DistanceHypothesiser(init_predictor_EKF, updater_EKF, Mahalanobis(), missed_distance=5)),
    updater=updater_EKF,
    min_points=2
)

# ######################################################################

initiator_UKF = MultiMeasurementInitiator(
    GaussianState(
        np.array([[20], [0], [10], [0]]),  # Prior State
        np.diag([1, 1, 1, 1])),
    measurement_model=None,
    deleter=deleter,
    data_associator=GNNWith2DAssignment(
        DistanceHypothesiser(init_predictor_UKF, updater_UKF, Mahalanobis(), missed_distance=5)),
    updater=updater_UKF,
    min_points=2
)

# ######################################################################

initiator_ESIF = MultiMeasurementInitiator(
    GaussianState(
        np.array([[20], [0], [10], [0]]),  # Prior State
        np.diag([1, 1, 1, 1])),
    measurement_model=None,
    deleter=deleter,
    data_associator=GNNWith2DAssignment(
        DistanceHypothesiser(init_predictor_EKF, updater_ESIF, Mahalanobis(), missed_distance=5)),
    updater=updater_ESIF,
    min_points=2
)

# %%
# The initiator for the particle filter works differently, so is shown below for clarity:
from stonesoup.initiator.simple import GaussianParticleInitiator
from stonesoup.types.state import GaussianState
from stonesoup.initiator.simple import SimpleMeasurementInitiator

prior_state = GaussianState(
    StateVector([20, 0, 10, 0]),
    np.diag([1, 1, 1, 1]) ** 2)

initiator_Part = SimpleMeasurementInitiator(prior_state, measurement_model=None,
                                            skip_non_reversible=True)
initiator_PF = GaussianParticleInitiator(number_particles=2000,
                                        initiator=initiator_Part,
                                        use_fixed_covar=False)

# %%
# Now we run the trackers and store the tracks in sets for plotting:
from stonesoup.tracker.simple import MultiTargetTracker

kalman_tracker_EKF = MultiTargetTracker(  # Runs the tracker
    initiator=initiator_EKF,
    deleter=deleter,
    detector=detectors[0],
    data_associator=data_associator_EKF,
    updater=updater_EKF,
)

tracks_EKF = set()
for step, (time, current_tracks) in enumerate(kalman_tracker_EKF, 1):
    tracks_EKF.update(current_tracks)

    # Stores the tracks in a set for plotting

# #######################################################################

kalman_tracker_UKF = MultiTargetTracker(
    initiator=initiator_UKF,
    deleter=deleter,
    detector=detectors[1],
    data_associator=data_associator_UKF,
    updater=updater_UKF,
)

tracks_UKF = set()
for step, (time, current_tracks) in enumerate(kalman_tracker_UKF, 1):
    tracks_UKF.update(current_tracks)

# ##########################################################################

kalman_tracker_ESIF = MultiTargetTracker(
    initiator=initiator_ESIF,
    deleter=deleter,
    detector=detectors[2],
    data_associator=data_associator_EKF,
    updater=updater_ESIF,
)

tracks_ESIF = set()
for step, (time, current_tracks) in enumerate(kalman_tracker_ESIF, 1):
    tracks_ESIF.update(current_tracks)

# ##########################################################################

tracker_PF = MultiTargetTracker(
    initiator=initiator_PF,
    deleter=deleter,
    detector=detectors[3],
    data_associator=data_associator_PF,
    updater=updater_PF,
)

tracks_PF = set()
for step, (time, current_tracks) in enumerate(tracker_PF, 1):
    tracks_PF.update(current_tracks)

# %%
# Finally, we plot the results:
plotter.plot_tracks(tracks_EKF, [0, 2], track_label="EKF", line=dict(color="orange"),
                    uncertainty=False)
plotter.plot_tracks(tracks_UKF, [0, 2], track_label="UKF", line=dict(color="blue"),
                    uncertainty=False)
plotter.plot_tracks(tracks_PF, [0, 2], track_label="PF", line=dict(color="brown"),
                    uncertainty=False)
plotter.plot_tracks(tracks_ESIF, [0, 2], track_label="ESIF", line=dict(color="green"),
                    uncertainty=False)
plotter.fig

# %%
# 4) Calculate and display metrics to show effectiveness of different tracking algorithms
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# The final part of this example is to calculate metrics that can determine how well each tracking
# algorithm followed the target. None will be perfect due to the sensor measurement noise and error
# in data association where multiple tracks meet, but some will perform better than others.
# 
# This section of the example follows code from the metrics example, which is also used in
# the sensor management tutorials. More complete documentation can be found there.
# 
# Firstly, we calculate the Optimal Sub-Pattern Assignment (OSPA) distance at each time
# step for each tracker. This is a measure of how far the calculated tracks are
# from the ground truth. We first initialise the metrics before plotting:

tracking_filters = ['EKF', 'UKF', 'PF', 'ESIF']

# %%
from stonesoup.metricgenerator.ospametric import OSPAMetric

ospa_generators = [OSPAMetric(c=40, p=1,
                              generator_name=f'{tracking_filter} OSPA metrics',
                              tracks_key=f'tracks_{tracking_filter}',
                              truths_key='truths'
                             )
                   for tracking_filter in tracking_filters]

from stonesoup.metricgenerator.tracktotruthmetrics import SIAPMetrics
from stonesoup.measures import Euclidean

siap_generators = [SIAPMetrics(position_measure=Euclidean((0, 2)),
                             velocity_measure=Euclidean((1, 3)),
                             generator_name=f'{tracking_filter} SIAP metrics',
                             tracks_key=f'tracks_{tracking_filter}',
                             truths_key='truths'
                            )
                  for tracking_filter in tracking_filters]


from stonesoup.metricgenerator.uncertaintymetric import SumofCovarianceNormsMetric

uncertainty_generators = [
    SumofCovarianceNormsMetric(generator_name=f'{tracking_filter} OSPA metrics',
                               tracks_key=f'tracks_{tracking_filter}')
    for tracking_filter in tracking_filters]

# %%
# Now we initialise the metric manager and generate the metrics:

from stonesoup.dataassociator.tracktotrack import TrackToTruth
from stonesoup.metricgenerator.manager import MultiManager

associator = TrackToTruth(association_threshold=30)

generators = ospa_generators + siap_generators + uncertainty_generators
metric_manager = MultiManager(generators, associator=associator)

metric_manager.add_data({'truths': ground_truth,
                         'tracks_EKF': tracks_EKF,
                         'tracks_UKF': tracks_UKF,
                         'tracks_PF': tracks_PF,
                         'tracks_ESIF': tracks_ESIF
                         })
metrics = metric_manager.generate_metrics()

# %%
# Now we can plot the OSPA distance for each tracker:

from stonesoup.plotter import MetricPlotter

fig1 = MetricPlotter()
fig1.plot_metrics(metrics, metric_names=['OSPA distances'])

# %%
# It can be seen that the EKF, UKF, and Particle Filter all behave very similarly,
# whereas the ESIF has very poor relative performance. A singular performance metric is calculated
# from this by summing the OSPA value over all timesteps:

# sum up distance error from ground truth over all timestamps
for tracking_filter in tracking_filters:
    total = sum([metrics[f'{tracking_filter} OSPA metrics']['OSPA distances'].value[i].value
                 for i in range(0, len(metrics[f'{tracking_filter} OSPA metrics']['OSPA distances'].value))])
    print(f'OSPA total value for {tracking_filter} is {total:.3f}')

# %%
# Finally, we calculate the SIAP metrics for the EKF. The same metrics can be calculated for the
# other trackers if desired. The user can copy this section of code and replace the relevant
# variable names to get the full metrics for the UKF, ESIF, and Particle Filter.
from stonesoup.metricgenerator.metrictables import SIAPTableGenerator

# generate metrics for EKF
siap_metrics = metrics['EKF SIAP metrics']
siap_averages_EKF = {siap_metrics.get(metric) for metric in siap_metrics
                     if metric.startswith("SIAP") and not metric.endswith(" at times")}

_ = SIAPTableGenerator(siap_averages_EKF).compute_metric()
print("\n\nSIAP metrics for EKF:")