#!/usr/bin/env python
"""
=============================================================
10 - Tracking in simulation: bringing all components together
=============================================================
The previous tutorials have introduced various aspects of Stone Soup covering inference and data
association for multiple-target trackers, using simulated data. This tutorial consolidates those
aspects in a notebook which can be modified to individual need. It contains all aspects
introduced in previous tutorials, and nothing new.
"""
# %%
# Process
# -------
# This notebook, as with the previous, proceeds according to the following steps:
#
# 1. Create the simulation
#
# * Initialise the 'playing field'
# * Choose number of targets and initial states
# * Create some transition models
# * Create some sensor models
#
# 2. Initialise the tracker components
#
# * Initialise predictors
# * Initialise updaters
# * Initialise data associations, hypothesisers
# * Initiators and deleters
# * Create the tracker
#
# 3. Run the tracker
#
# * Plot the output
#
# %%
# Create the simulation
# -----------------------
# %%
# Separate out the imports
import numpy as np
import datetime
# %%
# Initialise ground truth
# ^^^^^^^^^^^^^^^^^^^^^^^
# Here are some configurable parameters associated with the ground truth, e.g. defining where
# tracks are born and at what rate, death probability. This follows similar logic to the code
# in previous tutorial section :ref:`auto_tutorials/09_Initiators_&_Deleters:Simulating Multiple
# Targets`.
from stonesoup.types.array import StateVector, CovarianceMatrix
from stonesoup.types.state import GaussianState
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=5)
number_of_steps = 20
birth_rate = 0.3
death_probability = 0.05
initial_state = GaussianState(initial_state_mean, initial_state_covariance)
# %%
# Create the transition model - default set to 2d nearly-constant velocity with small (0.05)
# variance.
from stonesoup.models.transition.linear import (
CombinedLinearGaussianTransitionModel, ConstantVelocity)
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(0.05), ConstantVelocity(0.05)])
# %%
# Put this all together in a multi-target simulator.
from stonesoup.simulator.simple import MultiTargetGroundTruthSimulator
groundtruth_sim = MultiTargetGroundTruthSimulator(
transition_model=transition_model,
initial_state=initial_state,
timestep=timestep_size,
number_steps=number_of_steps,
birth_rate=birth_rate,
death_probability=death_probability
)
# %%
# Initialise the measurement models
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# The simulated ground truth will then be passed to a simple detection simulator. This again has a
# number of configurable parameters, e.g. where clutter is generated and at what rate, and
# detection probability. This implements similar logic to the code in the previous tutorial section
# :ref:`auto_tutorials/09_Initiators_&_Deleters:Generate Detections and Clutter`.
from stonesoup.simulator.simple import SimpleDetectionSimulator
from stonesoup.models.measurement.linear import LinearGaussian
# initialise the measurement model
measurement_model_covariance = np.diag([0.25, 0.25])
measurement_model = LinearGaussian(4, [0, 2], measurement_model_covariance)
# probability of detection
probability_detection = 0.9
# clutter will be generated uniformly in this are around the target
clutter_area = np.array([[-1, 1], [-1, 1]])*30
clutter_rate = 1
# %%
# The detection simulator
detection_sim = SimpleDetectionSimulator(
groundtruth=groundtruth_sim,
measurement_model=measurement_model,
detection_probability=probability_detection,
meas_range=clutter_area,
clutter_rate=clutter_rate
)
# %%
# Create the tracker components
# -----------------------------
# In this example a Kalman filter is used with global nearest neighbour (GNN) associator. Other
# options are, of course, available.
#
# %%
# Predictor
# ^^^^^^^^^
# Initialise the predictor using the same transition model as generated the ground truth. Note you
# don't have to use the same model.
from stonesoup.predictor.kalman import KalmanPredictor
predictor = KalmanPredictor(transition_model)
# %%
# Updater
# ^^^^^^^
# Initialise the updater using the same measurement model as generated the simulated detections.
# Note, again, you don't have to use the same model (noise covariance).
from stonesoup.updater.kalman import KalmanUpdater
updater = KalmanUpdater(measurement_model)
# %%
# Data associator
# ^^^^^^^^^^^^^^^
# Initialise a hypothesiser which will rank predicted measurement - measurement pairs according to
# some measure.
# Initialise a Mahalanobis distance measure to facilitate this ranking.
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
hypothesiser = DistanceHypothesiser(predictor, updater, measure=Mahalanobis(), missed_distance=3)
# %%
# Initialise the GNN with the hypothesiser.
from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
data_associator = GNNWith2DAssignment(hypothesiser)
# %%
# Initiator and Deleter
# ^^^^^^^^^^^^^^^^^^^^^
# Create deleter - get rid of anything with a covariance trace greater than 2
from stonesoup.deleter.error import CovarianceBasedDeleter
covariance_limit_for_delete = 2
deleter = CovarianceBasedDeleter(covar_trace_thresh=covariance_limit_for_delete)
# %%
# Set a standard prior state and the minimum number of detections required to qualify for
# initiation
s_prior_state = GaussianState([[0], [0], [0], [0]], np.diag([0, 0.5, 0, 0.5]))
min_detections = 3
# %%
# Initialise the initiator - use the 'full tracker' components specified above in the initiator.
# But note that other ones could be used if needed.
from stonesoup.initiator.simple import MultiMeasurementInitiator
initiator = MultiMeasurementInitiator(
prior_state=s_prior_state,
measurement_model=measurement_model,
deleter=deleter,
data_associator=data_associator,
updater=updater,
min_points=min_detections
)
# %%
# Run the Tracker
# ---------------
# With the components created, the multi-target tracker component is created, constructed from
# the components specified above. This is logically the same as tracking code in the previous
# tutorial section :ref:`auto_tutorials/09_Initiators_&_Deleters:Running the Tracker`
from stonesoup.tracker.simple import MultiTargetTracker
tracker = MultiTargetTracker(
initiator=initiator,
deleter=deleter,
detector=detection_sim,
data_associator=data_associator,
updater=updater,
)
# %%
# In the case of using (J)PDA like in :ref:`auto_tutorials/07_PDATutorial:Run the PDA Filter`
# and :ref:`auto_tutorials/08_JPDATutorial:Running the JPDA filter`, then the
# :class:`~.MultiTargetMixtureTracker` would be used instead on the
# :class:`~.MultiTargetTracker` used above.
#
# Plot the outputs
# ^^^^^^^^^^^^^^^^
# We plot the ground truth, detections and the tracker output using the Stone Soup :class:`Plotter`.
groundtruth = set()
detections = set()
tracks = set()
for time, ctracks in tracker:
groundtruth.update(groundtruth_sim.groundtruth_paths)
detections.update(detection_sim.detections)
tracks.update(ctracks)
# %%
from stonesoup.plotter import Plotterly
plotter = Plotterly()
plotter.plot_ground_truths(groundtruth, mapping=[0, 2])
plotter.plot_measurements(detections, mapping=[0, 2])
plotter.plot_tracks(tracks, mapping=[0, 2])
plotter.fig