translation_offset=np.array([[sensor_x], [sensor_y]]))
# %%
# Populate the measurement array
measurements = []
for state in truth:
measurement = measurement_model.function(state, noise=True)
measurements.append(
Detection(measurement,
timestamp=state.timestamp,
measurement_model=measurement_model))
# %%
# Plot those measurements
plotter.plot_measurements(measurements, [0, 2])
plotter.fig
# %%
# Set up the particle filter
# ^^^^^^^^^^^^^^^^^^^^^^^^^^
# Analogously to the Kalman family, we create a :class:`~.ParticlePredictor` and a
# :class:`~.ParticleUpdater` which take responsibility for the predict and update steps
# respectively. These require a :class:`~.TransitionModel` and :class:`~.MeasurementModel` as
# before.
# To cope with sample sparsity we also include a resampler, in this instance
# :class:`~.SystematicResampler`, which is passed to the updater. It should be noted that there are
# many resampling schemes, and almost as many choices as to when to undertake resampling. The
# systematic resampler is described in [#]_, and in what follows below resampling is undertaken
# at each time-step.
from stonesoup.predictor.particle import ParticlePredictor
timestamp=truth_state.timestamp,
measurement_model=measurement_model))
# Generate clutter at this time-step
for _ in range(np.random.poisson(clutter_rate)):
x = uniform.rvs(-100, 200)
y = uniform.rvs(-100, 200)
measurement_set.add(
Clutter(np.array([[x], [y]]),
timestamp=timestamp,
measurement_model=measurement_model))
all_measurements.append(measurement_set)
# Plot true detections and clutter.
plotter.plot_measurements(all_measurements, [0, 2], color='g')
plotter.fig
# %%
# Create the Predicter and Updater
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# The updater is a :class:`~.PHDUpdater`, and since it uses the mixed Gaussian paths, it is a
# GM-PHD updater. For each individual track we use a :class:`~.KalmanUpdater`. Here we assume
# that the measurement range and clutter spatial density are known to the filter. We
# invite you to change these variables to mimic removing this assumption.
from stonesoup.updater.kalman import KalmanUpdater
kalman_updater = KalmanUpdater(measurement_model)
# Area in which we look for target. Note that if a target appears outside of this area the
# filter will not pick up on it.