# Set a constant velocity transition model for the targets
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(0.5),
ConstantVelocity(0.5),
ConstantVelocity(0.1)])
# Define the Gaussian State from which new targets are sampled on initialisation
initial_target_state = GaussianState(
StateVector([[0], [0], [0], [0], [9000], [0]]),
CovarianceMatrix(np.diag([2000, 50, 2000, 50, 100, 1])))
groundtruth_sim = MultiTargetGroundTruthSimulator(
transition_model=transition_model, # target transition model
initial_state=initial_target_state, # add our initial state for targets
timestep=timedelta(seconds=1), # time between measurements
number_steps=120, # 2 minute
birth_rate=0.10, # 10% chance of a new target being born
death_probability=0.01 # 1% chance of a target being killed
)
# %%
# Now that we have set up our ground truth simulation we need to add our platform into the simulation capability. This
# is done using the :class:`~.PlatformDetectionSimulator`. This simulator allows a *list* of platforms to be added into
# the simulation, when the simulation is processed the platforms are able to make detections of both the ground truth
# targets and other platforms.
#
# In this case we have a single platform, therefore the radar sensor on this platform will only be able to make
# measurements of the ground truth objects generated by the simulator.
# Import the PlatformDetectionSimulator
from stonesoup.simulator.platform import PlatformDetectionSimulator
from stonesoup.types.state import GaussianState
from stonesoup.updater.kalman import KalmanUpdater
# Models
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(1), ConstantVelocity(1)], seed=1)
measurement_model = LinearGaussian(4, [0, 2], np.diag([0.5, 0.5]), seed=2)
# Simulators
groundtruth_sim = MultiTargetGroundTruthSimulator(
transition_model=transition_model,
initial_state=GaussianState(
StateVector([[0], [0], [0], [0]]),
CovarianceMatrix(np.diag([1000, 10, 1000, 10]))),
timestep=datetime.timedelta(seconds=5),
number_steps=100,
birth_rate=0.2,
death_probability=0.05,
seed=3
)
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
# %%
# 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