Source code for stonesoup.hypothesiser.mfa

# -*- coding: utf-8 -*-
from .base import Hypothesiser
from ..base import Property
from ..types.multihypothesis import MultipleHypothesis
from ..types.prediction import TaggedWeightedGaussianStatePrediction

[docs] class MFAHypothesiser(Hypothesiser): """Multi-Frame Assignment Hypothesiser based on an underlying Hypothesiser Generates a list of SingleHypotheses pertaining to individual component-detection hypotheses Note ---- This is to be used in conjunction with the :class:`~.MFADataAssociator` References ---------- 1. Xia, Y., Granström, K., Svensson, L., García-Fernández, Á.F., and Williams, J.L., 2019. Multiscan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter. J. Adv. Information Fusion, 14(2), pp. 213–235. """ hypothesiser: Hypothesiser = Property( doc="Underlying hypothesiser used to generate detection-target pairs")
[docs] def hypothesise(self, track, detections, timestamp, detections_tuple, **kwargs): """Form hypotheses for associations between Detections and a given track. Parameters ---------- track: :class:`~.Track` The track object to hypothesise on detections : set of :class:`~.Detection` Retrieved measurements timestamp : datetime Time of the detections/predicted states detections_tuple : tuple of :class:`~.Detection` Original tuple of detections required for consistent indexing Returns ------- : :class:`~.MultipleHypothesis` A container of :class:`~.SingleProbabilityHypothesis` objects, pertaining to individual component-detection hypotheses """ # Check to make sure all detections are obtained from the same time timestamps = {detection.timestamp for detection in detections} if len(timestamps) > 1: raise ValueError("All detections must have the same timestamp") hypotheses = list() for component in track.state.components: # Get hypotheses for that component for all measurements component_hypotheses = self.hypothesiser.hypothesise( component, detections, timestamp, **kwargs) for hypothesis in component_hypotheses: # Update component tag and weight det_idx = detections_tuple.index(hypothesis.measurement) + 1 if hypothesis else 0 new_weight = component.weight * hypothesis.weight hypothesis.prediction = \ TaggedWeightedGaussianStatePrediction( tag=[*component.tag, det_idx], # TODO: Avoid dependency on indexes weight=new_weight, state_vector=hypothesis.prediction.state_vector, covar=hypothesis.prediction.covar, timestamp=hypothesis.prediction.timestamp ) hypotheses.append(hypothesis) # Create Multiple Hypothesis and add to list hypotheses = MultipleHypothesis(hypotheses) return hypotheses