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JPDA and SystematicResampler Fixes (updated) #193

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May 6, 2020
Merged

JPDA and SystematicResampler Fixes (updated) #193

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fab6792
Sort particles by weight before cumsum.
JPompeus Nov 26, 2019
18b8ac4
JPDA to handle asynchronous measurements
JPompeus Nov 26, 2019
254a91f
Minor fixes and update
sglvladi Apr 15, 2020
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17 changes: 8 additions & 9 deletions stonesoup/dataassociator/probability.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,25 +129,24 @@ def associate(self, tracks, detections, time):
SingleProbabilityHypothesis(
hypotheses[track][0].prediction,
MissedDetection(timestamp=time),
measurement_prediction=hypotheses[track][0]
.measurement_prediction,
measurement_prediction=hypotheses[track][0].measurement_prediction,
probability=prob_misdetect))

# record hypothesis for any given Detection being associated with
# this track
for detection in detections:
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 detection)
if joint_hypothesis.hypotheses[track].measurement is hypothesis.measurement)

single_measurement_hypotheses.append(
SingleProbabilityHypothesis(
hypotheses[track][0].prediction,
detection,
measurement_prediction=hypotheses[track][0].
measurement_prediction,
hypothesis.prediction,
hypothesis.measurement,
measurement_prediction=hypothesis.measurement_prediction,
probability=pro_detect_assoc))

result = MultipleHypothesis(single_measurement_hypotheses, True, 1)
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8 changes: 5 additions & 3 deletions stonesoup/resampler/particle.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
# -*- coding: utf-8 -*-
import numpy as np
from operator import attrgetter

from .base import Resampler
from ..types.numeric import Probability
Expand All @@ -24,8 +25,9 @@ def resample(self, particles):

n_particles = len(particles)
weight = Probability(1/n_particles)
cdf = np.cumsum([p.weight for p in particles])
particles_listed = list(particles)
particles_sorted = sorted(particles, key=attrgetter('weight'), reverse=False)
cdf = np.cumsum([p.weight for p in particles_sorted])

# Pick random starting point
u_i = np.random.uniform(0, 1 / n_particles)
new_particles = []
Expand All @@ -36,7 +38,7 @@ def resample(self, particles):

u_j = u_i + (1 / n_particles) * j

particle = particles_listed[np.argmax(u_j < cdf)]
particle = particles_sorted[np.argmax(u_j < cdf)]
new_particles.append(
Particle(particle.state_vector,
weight=weight,
Expand Down