from datetime import datetime
import numpy as np
# Simulate Data
from stonesoup.types.groundtruth import GroundTruthPath, GroundTruthState
# Figure to plot truth (and future data)
from matplotlib import pyplot as plt
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(1, 1, 1)
ax.set_xlabel("$x$")
ax.set_ylabel("$y$")
truth = GroundTruthPath()
start_time = datetime.now()
for n in range(1, 21):
x = n
y = n
varxy = np.array([[0.1, 0], [0, 0.1]])
xy = np.random.multivariate_normal(np.array([x, y]), varxy)
truth.append(
GroundTruthState(np.array([[xy[0]], [xy[1]]]),
timestamp=start_time + timedelta(seconds=n)))
#Plot the result
ax.plot([state.state_vector[0, 0] for state in truth],
[state.state_vector[1, 0] for state in truth],
linestyle="--")
# As in previous tutorials, we start with a target moving linearly in the 2D cartesian plane.
import numpy as np
from scipy.stats import uniform
from datetime import datetime
from datetime import timedelta
from stonesoup.models.transition.linear import CombinedLinearGaussianTransitionModel, \
ConstantVelocity
from stonesoup.types.groundtruth import GroundTruthPath, GroundTruthState
np.random.seed(1991)
start_time = datetime.now()
transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(0.005),
ConstantVelocity(0.005)])
truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])
for k in range(1, 21):
truth.append(GroundTruthState(
transition_model.function(truth[k-1], noise=True, time_interval=timedelta(seconds=1)),
timestamp=start_time+timedelta(seconds=k)))
# %%
# Probability of detection
# ^^^^^^^^^^^^^^^^^^^^^^^^
# For the first time we introduce the possibility that, at any time-step, our sensor receives no
# detection from the target (i.e. :math:`p_d < 1`).
prob_det = 0.9
# %%
# Simulate clutter
# ^^^^^^^^^^^^^^^^
truth.append(
GroundTruthState(transition_model.function(
truth[-1], noise=True, time_interval=timedelta(seconds=1)),
timestamp=start_time + timedelta(seconds=k)))
# Birth
for _ in range(np.random.poisson(0.6)): # Birth probability
x, y = initial_position = np.random.rand(2) * [
20, 20
] # Range [0, 20] for x and y
x_vel, y_vel = (
np.random.rand(2)) * 2 - 1 # Range [-1, 1] for x and y velocity
state = GroundTruthState([x, x_vel, y, y_vel],
timestamp=start_time + timedelta(seconds=k))
# Add to truth set for current and for all timestamps
truth = GroundTruthPath([state])
current_truths.add(truth)
truths.add(truth)
from stonesoup.plotter import Plotter
plotter = Plotter()
plotter.ax.set_ylim(-5, 25)
plotter.plot_ground_truths(truths, [0, 2])
# %%
# Generate Detections and Clutter
# -------------------------------
# Next, generate detections with clutter just as in the previous tutorials, skipping over the truth
# paths that weren't alive at the current time step.
from scipy.stats import uniform
