# ^^^^^^^^^^^^^^^^^^^^^^^^^^ # 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 predictor = ParticlePredictor(transition_model) from stonesoup.resampler.particle import SystematicResampler resampler = SystematicResampler() from stonesoup.updater.particle import ParticleUpdater updater = ParticleUpdater(measurement_model, resampler) # %% # Initialise a prior # ^^^^^^^^^^^^^^^^^^ # To start we create a prior estimate. This is a set of :class:`~.Particle` and we sample from # Gaussian distribution (using the same parameters we had in the previous examples). from scipy.stats import multivariate_normal from stonesoup.types.particle import Particles from stonesoup.types.numeric import Probability # Similar to a float type from stonesoup.types.state import ParticleState
np.diag([1.5, 0.5, 1.5, 0.5]),
size=number_particles)
weight = Probability(1 / number_particles)
particles = [
Particle(sample.reshape(-1, 1), weight=weight) for sample in samples
]
# %%
from stonesoup.resampler.particle import SystematicResampler
from stonesoup.types.track import Track
from stonesoup.dataassociator.tracktotrack import TrackToTruth
from stonesoup.metricgenerator.manager import SimpleManager
from stonesoup.metricgenerator.tracktotruthmetrics import SIAPMetrics
from stonesoup.plotter import Plotter
updaters[0].resampler = SystematicResampler(
) # Allow particle filter to re-sample
pa = dict()
siap_gen = SIAPMetrics(position_mapping=[0, 2])
for predictor, updater, colour, filter, particle_count \
in zip(predictors, updaters, colours, filters, particle_counts):
track = Track()
prior = ParticleState(particles[:particle_count], timestamp=start_time)
for measurement in measurements:
prediction = predictor.predict(prior, timestamp=measurement.timestamp)
hypothesis = SingleHypothesis(prediction, measurement)
post = updater.update(hypothesis)
track.append(post)
prior = track[-1]