import numpy as np
from stonesoup.types.detection import GaussianDetection
from stonesoup.feeder.base import DetectionFeeder
from stonesoup.models.measurement.linear import LinearGaussian
from ..buffered_generator import BufferedGenerator
[docs]class Tracks2GaussianDetectionFeeder(DetectionFeeder):
'''
Feeder consumes Track objects and outputs GaussianDetection objects.
At each time step, the :attr:`Reader` feeds in a set of live tracks. The feeder takes the most
recent state from each of those tracks, and turn them into a set of
:class:`~.GaussianDetection` objects. Each detection is given a :class:`~.LinearGaussian`
measurement model whose covariance is equal to the state covariance. The feeder assumes that
the tracks are all live, that is each track has a state at the most recent time step.
'''
@BufferedGenerator.generator_method
def data_gen(self):
for time, tracks in self.reader:
detections = []
for track in tracks:
dim = len(track.state.state_vector)
detections.append(
GaussianDetection.from_state(
track.state,
measurement_model=LinearGaussian(
dim, list(range(dim)), np.asarray(track.covar)),
target_type=GaussianDetection)
)
yield time, detections