# Source code for stonesoup.dataassociator.probability

```
# -*- coding: utf-8 -*-
from .base import DataAssociator
from ..base import Property
from ..hypothesiser import Hypothesiser
from ..hypothesiser.probability import PDAHypothesiser
from ..types.detection import MissedDetection
from ..types.hypothesis import (
SingleProbabilityHypothesis, ProbabilityJointHypothesis)
from ..types.multihypothesis import MultipleHypothesis
from ..types.numeric import Probability
import itertools
[docs]class PDA(DataAssociator):
"""Probabilistic Data Association (PDA)
Given a set of detections and a set of tracks, each track has a
probability that it is associated to each specific detection.
"""
hypothesiser: Hypothesiser = Property(
doc="Generate a set of hypotheses for each prediction-detection pair")
[docs] def associate(self, tracks, detections, timestamp, **kwargs):
# Generate a set of hypotheses for each track on each detection
hypotheses = self.generate_hypotheses(tracks, detections, timestamp, **kwargs)
# Ensure association probabilities are normalised
for track, hypothesis in hypotheses.items():
hypothesis.normalise_probabilities(total_weight=1)
return hypotheses
[docs]class JPDA(DataAssociator):
r"""Joint Probabilistic Data Association (JPDA)
Given a set of Detections and a set of Tracks, each Detection has a
probability that it is associated with each specific Track. Rather than
associate specific Detections/Tracks, JPDA calculates the new state of a
Track based on its possible association with ALL Detections. The new
state is a Gaussian Mixture, reduced to a single Gaussian.
If
.. math::
prob_{association(Detection, Track)} <
\frac{prob_{association(MissedDetection, Track)}}{gate\ ratio}
then Detection is assumed to be outside Track's gate, and the probability
of association is dropped from the Gaussian Mixture. This calculation
takes place in the function :meth:`enumerate_JPDA_hypotheses`.
"""
hypothesiser: PDAHypothesiser = Property(
doc="Generate a set of hypotheses for each prediction-detection pair")
[docs] def associate(self, tracks, detections, timestamp, **kwargs):
# Calculate MultipleHypothesis for each Track over all
# available Detections
hypotheses = self.generate_hypotheses(tracks, detections, timestamp, **kwargs)
# enumerate the Joint Hypotheses of track/detection associations
joint_hypotheses = \
self.enumerate_JPDA_hypotheses(tracks, hypotheses)
# Calculate MultiMeasurementHypothesis for each Track over all
# available Detections with probabilities drawn from JointHypotheses
new_hypotheses = dict()
for track in tracks:
single_measurement_hypotheses = list()
# record the MissedDetection hypothesis for this track
prob_misdetect = Probability.sum(
joint_hypothesis.probability
for joint_hypothesis in joint_hypotheses
if not joint_hypothesis.hypotheses[track].measurement)
single_measurement_hypotheses.append(
SingleProbabilityHypothesis(
hypotheses[track][0].prediction,
MissedDetection(timestamp=timestamp),
measurement_prediction=hypotheses[track][0].measurement_prediction,
probability=prob_misdetect))
# record hypothesis for any given Detection being associated with
# this track
for hypothesis in hypotheses[track]:
if not hypothesis:
continue
pro_detect_assoc = Probability.sum(
joint_hypothesis.probability
for joint_hypothesis in joint_hypotheses
if joint_hypothesis.hypotheses[track].measurement is hypothesis.measurement)
single_measurement_hypotheses.append(
SingleProbabilityHypothesis(
hypothesis.prediction,
hypothesis.measurement,
measurement_prediction=hypothesis.measurement_prediction,
probability=pro_detect_assoc))
result = MultipleHypothesis(single_measurement_hypotheses, True, 1)
new_hypotheses[track] = result
return new_hypotheses
@classmethod
def enumerate_JPDA_hypotheses(cls, tracks, multihypths):
joint_hypotheses = list()
if not tracks:
return joint_hypotheses
# perform a simple level of gating - all track/detection pairs for
# which the probability of association is a certain multiple less
# than the probability of missed detection - detection is outside the
# gating region, association is impossible
possible_assoc = list()
for track in tracks:
track_possible_assoc = list()
for hypothesis in multihypths[track]:
# Always include missed detection (gate ratio < 1)
track_possible_assoc.append(hypothesis)
possible_assoc.append(track_possible_assoc)
# enumerate all valid JPDA joint hypotheses
enum_JPDA_hypotheses = (
joint_hypothesis
for joint_hypothesis in itertools.product(*possible_assoc)
if cls.isvalid(joint_hypothesis))
# turn the valid JPDA joint hypotheses into 'JointHypothesis'
for joint_hypothesis in enum_JPDA_hypotheses:
local_hypotheses = {}
for track, hypothesis in zip(tracks, joint_hypothesis):
local_hypotheses[track] = \
multihypths[track][hypothesis.measurement]
joint_hypotheses.append(
ProbabilityJointHypothesis(local_hypotheses))
# normalize ProbabilityJointHypotheses relative to each other
sum_probabilities = Probability.sum(hypothesis.probability
for hypothesis in joint_hypotheses)
for hypothesis in joint_hypotheses:
hypothesis.probability /= sum_probabilities
return joint_hypotheses
@staticmethod
def isvalid(joint_hypothesis):
# 'joint_hypothesis' represents a valid joint hypothesis if
# no measurement is repeated (ignoring missed detections)
measurements = set()
for hypothesis in joint_hypothesis:
measurement = hypothesis.measurement
if not measurement:
pass
elif measurement in measurements:
return False
else:
measurements.add(measurement)
return True
```