def build_platform(sensors, x_velocity):
state = State(state_vector=[[0], [x_velocity], [0], [0]],
timestamp=datetime.datetime(2020, 4, 1))
model_1d = ConstantVelocity(0.0) # zero noise so pure movement
trans_model = CombinedLinearGaussianTransitionModel([model_1d] * 2)
position_mapping = np.array([[0, 2]])
platform = MovingPlatform(states=state,
sensors=sensors,
transition_model=trans_model,
position_mapping=position_mapping)
return platform
from stonesoup.models.transition.linear import (
CombinedLinearGaussianTransitionModel, ConstantVelocity)
from stonesoup.platform.base import MovingPlatform
from stonesoup.types.state import State
time_step = datetime.timedelta(seconds=0.1)
time_init = datetime.datetime.now()
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(1.),
ConstantVelocity(1.),
ConstantVelocity(1.)])
red = MovingPlatform(position_mapping=[0, 2, 4],
velocity_mapping=[0, 2, 4],
states=State([50., 0., -50., 10., 0., 0.],
timestamp=time_init),
transition_model=transition_model)
# %%
# Move target
for s in range(1, 100):
red.move(time_init + s * time_step)
# %%
# Measure target states
# ^^^^^^^^^^^^^^^^^^^^^
# The states are measured with and without noise to show the real position with the measured one.
measurements = []
noiseless_measurements = []
# \end{bmatrix}
# First import the Moving platform
from stonesoup.platform.base import MovingPlatform
# Define the initial platform position, in this case the origin
initial_loc = StateVector([[0], [0], [0], [50], [8000], [0]])
initial_state = State(initial_loc, start_time)
# Define transition model and position for 3D platform
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(0.), ConstantVelocity(0.), ConstantVelocity(0.)])
# create our fixed platform
sensor_platform = MovingPlatform(states=initial_state,
position_mapping=(0, 2, 4),
velocity_mapping=(1, 3, 5),
transition_model=transition_model)
# %%
# With our platform generated we now need to build a set of sensors which will be mounted onto the platform. In this
# case we will exploit a :class:`~.RadarElevationBearingRangeRate` and a :class:`~.PassiveElevationBearing` sensor
# (e.g. an optical sensor, which has no capability to directly measure range).
#
# First we will create a radar which is capable of measuring bearing (:math:`\phi`), elevation (:math:`\theta`), range
# (:math:`r`) and range-rate (:math:`\dot{r}`) of the target platform.
# Import a range rate bearing elevation capable radar
from stonesoup.sensor.radar.radar import RadarElevationBearingRangeRate
# Create a radar sensor
radar_noise_covar = CovarianceMatrix(np.diag(
# First import the Moving platform
from stonesoup.platform.base import MovingPlatform
# Define the initial platform position, in this case the origin
initial_loc = StateVector([[0], [0], [0], [50], [8000], [0]])
initial_state = State(initial_loc, start_time)
# Define transition model and position for 3D platform
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(0.),
ConstantVelocity(0.),
ConstantVelocity(0.)])
# create our fixed platform
sensor_platform = MovingPlatform(states=initial_state,
position_mapping=(0, 2, 4),
velocity_mapping=(1, 3, 5),
transition_model=transition_model)
# %%
# With our platform generated we now need to build a set of sensors which will be mounted onto the platform. In this
# case we will exploit a :class:`~.RadarElevationBearingRangeRate` and a :class:`~.PassiveElevationBearing` sensor
# (e.g. an optical sensor, which has no capability to directly measure range).
#
# First we will create a radar which is capable of measuring bearing (:math:`\phi`), elevation (:math:`\theta`), range
# (:math:`r`) and range-rate (:math:`\dot{r}`) of the target platform.
# Import a range rate bearing elevation capable radar
from stonesoup.sensor.radar.radar import RadarElevationBearingRangeRate
# Create a radar sensor
radar_noise_covar = CovarianceMatrix(