timestamp = start_time + timedelta(seconds=k)
for truth in truths:
try:
truth_state = truth[timestamp]
except IndexError:
# This truth not alive at this time. Skip this iteration of the for loop.
continue
# Generate actual detection from the state with a 10% chance that no detection is received.
if np.random.rand() <= probability_detection:
# Generate actual detection from the state
measurement = measurement_model.function(truth_state, noise=True)
measurement_set.add(
TrueDetection(state_vector=measurement,
groundtruth_path=truth,
timestamp=truth_state.timestamp,
measurement_model=measurement_model))
# Generate clutter at this time-step
for _ in range(np.random.poisson(clutter_rate)):
x = uniform.rvs(-100, 200)
y = uniform.rvs(-100, 200)
measurement_set.add(
Clutter(np.array([[x], [y]]),
timestamp=timestamp,
measurement_model=measurement_model))
all_measurements.append(measurement_set)
# Plot true detections and clutter.
plotter.plot_measurements(all_measurements, [0, 2], color='g')
mapping=(0, 2),
noise_covar=np.array([[0.75, 0], [0,
0.75]]))
prob_detect = 0.9 # 90% chance of detection.
for k in range(20):
measurement_set = set()
for truth in truths:
# Generate actual detection from the state with a 10% chance that no detection is received.
if np.random.rand() <= prob_detect:
measurement = measurement_model.function(truth[k], noise=True)
measurement_set.add(
TrueDetection(state_vector=measurement,
groundtruth_path=truth,
timestamp=truth[k].timestamp))
# Generate clutter at this time-step
truth_x = truth[k].state_vector[0]
truth_y = truth[k].state_vector[2]
for _ in range(np.random.randint(10)):
x = uniform.rvs(truth_x - 10, 20)
y = uniform.rvs(truth_y - 10, 20)
measurement_set.add(
Clutter(np.array([[x], [y]]), timestamp=truth[k].timestamp))
all_measurements.append(measurement_set)
# Plot measurements.
for set_ in all_measurements:
# Plot actual detections.