[5, 0], # Covariance matrix for Gaussian PDF
[0, 5]
]))
# %%
# Check the output is as we expect
measurement_model.matrix()
# %%
measurement_model.covar()
# %%
# Generate the measurements
measurements = []
for state in truth:
measurement = measurement_model.function(state, noise=True)
measurements.append(Detection(measurement, timestamp=state.timestamp))
# Plot the result
ax.scatter([state.state_vector[0] for state in measurements],
[state.state_vector[1] for state in measurements],
color='b')
fig
# %%
# At this stage you should have a moderately linear ground truth path (dotted line) with a series
# of simulated measurements overplotted (blue circles). Take a moment to fiddle with the numbers in
# :math:`Q` and :math:`R` to see what it does to the path and measurements.
# %%
# Construct a Kalman filter
def generate_scenario_3(seed=1996,
permanent_save=True,
radar_meas_rate=1,
ais_meas_rate=5,
sigma_process=0.01,
sigma_meas_radar=3,
sigma_meas_ais=1,
timesteps=20):
"""
Generates scenario 3. Scenario 3 consists of radar and ais measurements with different sampling rate. The sampling
rate is specified in the input params. A groundtruth is generated for each second.
:param seed:
:param permanent_save:
:param radar_meas_rate:
:param ais_meas_rate:
:param sigma_process:
:param sigma_meas_radar:
:param sigma_meas_ais:
:param timesteps: The amount of measurements from the slowest sensor
:return: Nothing. Saves the scenario to a specified folder
"""
start_time = datetime.now()
# specify seed to be able to repeat the example
np.random.seed(seed)
# combine two 1-D CV models to create a 2-D CV model
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(sigma_process),
ConstantVelocity(sigma_process)])
# starting at 0,0 and moving NE
truth = GroundTruthPath(
[GroundTruthState([0, 1, 0, 1], timestamp=start_time)])
# generate truth using transition_model and noise
end_time = start_time + timedelta(seconds=timesteps *
max(radar_meas_rate, ais_meas_rate))
time = start_time + timedelta(seconds=1)
while time < end_time:
truth.append(
GroundTruthState(transition_model.function(
truth[-1], noise=True, time_interval=timedelta(seconds=1)),
timestamp=time))
time += timedelta(seconds=1)
# Simulate measurements
# Specify measurement model for radar
measurement_model_radar = LinearGaussian(
ndim_state=4, # number of state dimensions
mapping=(0, 2), # mapping measurement vector index to state index
noise_covar=np.array([
[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF
[0, sigma_meas_radar]
]))
# Specify measurement model for AIS (Same as for radar)
measurement_model_ais = LinearGaussian(ndim_state=4,
mapping=(0, 2),
noise_covar=np.array(
[[sigma_meas_ais, 0],
[0, sigma_meas_ais]]))
# generate "radar" measurements
measurements_radar = []
measurements_ais = []
next_radar_meas_time = start_time
next_ais_meas_time = start_time
for state in truth:
# check whether we want to generate a measurement from this gt
if state.timestamp == next_radar_meas_time:
measurement = measurement_model_radar.function(state, noise=True)
measurements_radar.append(
Detection(measurement, timestamp=state.timestamp))
next_radar_meas_time += timedelta(seconds=radar_meas_rate)
if state.timestamp == next_ais_meas_time:
measurement = measurement_model_ais.function(state, noise=True)
measurements_ais.append(
Detection(measurement, timestamp=state.timestamp))
next_ais_meas_time += timedelta(seconds=ais_meas_rate)
if permanent_save:
save_folder_name = seed.__str__()
else:
save_folder_name = "temp"
save_folder = "../scenarios/scenario3/" + save_folder_name + "/"
# save the ground truth and the measurements for the radar and the AIS
store_object.store_object(truth, save_folder, "ground_truth.pk1")
store_object.store_object(measurements_radar, save_folder,
"measurements_radar.pk1")
store_object.store_object(measurements_ais, save_folder,
"measurements_ais.pk1")
store_object.store_object(start_time, save_folder, "start_time.pk1")
store_object.store_object(measurement_model_radar, save_folder,
"measurement_model_radar.pk1")
store_object.store_object(measurement_model_ais, save_folder,
"measurement_model_ais.pk1")
store_object.store_object(transition_model, save_folder,
"transition_model.pk1")
measurement_model = LinearGaussian(
ndim_state=4,
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,
measurement_model=measurement_model))
# 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,
measurement_model=measurement_model))
all_measurements.append(measurement_set)
def generate_scenario_1(seed=1996,
permanent_save=True,
sigma_process=0.01,
sigma_meas_radar=3,
sigma_meas_ais=1):
"""
Generates scenario 1. Todo define scenario 1
:param seed:
:param permanent_save:
:param sigma_process:
:param sigma_meas_radar:
:param sigma_meas_ais:
:return:
"""
# specify seed to be able repeat example
start_time = datetime.now()
np.random.seed(seed)
# combine two 1-D CV models to create a 2-D CV model
transition_model = CombinedLinearGaussianTransitionModel(
[ConstantVelocity(sigma_process),
ConstantVelocity(sigma_process)])
# starting at 0,0 and moving NE
truth = GroundTruthPath(
[GroundTruthState([0, 1, 0, 1], timestamp=start_time)])
# generate truth using transition_model and noise
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)))
# Simulate measurements
# Specify measurement model for radar
measurement_model_radar = LinearGaussian(
ndim_state=4, # number of state dimensions
mapping=(0, 2), # mapping measurement vector index to state index
noise_covar=np.array([
[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF
[0, sigma_meas_radar]
]))
# Specify measurement model for AIS
measurement_model_ais = LinearGaussian(ndim_state=4,
mapping=(0, 2),
noise_covar=np.array(
[[sigma_meas_ais, 0],
[0, sigma_meas_ais]]))
# generate "radar" measurements
measurements_radar = []
for state in truth:
measurement = measurement_model_radar.function(state, noise=True)
measurements_radar.append(
Detection(measurement, timestamp=state.timestamp))
# generate "AIS" measurements
measurements_ais = []
state_num = 0
for state in truth:
state_num += 1
if not state_num % 2: # measurement every second time step
measurement = measurement_model_ais.function(state, noise=True)
measurements_ais.append(
Detection(measurement, timestamp=state.timestamp))
if permanent_save:
save_folder_name = seed.__str__()
else:
save_folder_name = "temp"
save_folder = "../scenarios/scenario1/" + save_folder_name + "/"
# save the ground truth and the measurements for the radar and the AIS
store_object.store_object(truth, save_folder, "ground_truth.pk1")
store_object.store_object(measurements_radar, save_folder,
"measurements_radar.pk1")
store_object.store_object(measurements_ais, save_folder,
"measurements_ais.pk1")
store_object.store_object(start_time, save_folder, "start_time.pk1")
store_object.store_object(measurement_model_radar, save_folder,
"measurement_model_radar.pk1")
store_object.store_object(measurement_model_ais, save_folder,
"measurement_model_ais.pk1")
store_object.store_object(transition_model, save_folder,
"transition_model.pk1")
noise_covar=np.array([[1, 0], # covariance matrix for Gaussian PDF
[0, 1]])
)
# Specify measurement model for AIS (Same as for radar)
measurement_model_ais = LinearGaussian(
ndim_state=4,
mapping=(0, 2),
noise_covar=np.array([[1, 0],
[0, 1]])
)
# generate "radar" measurements
measurements_radar = []
for state in truth:
measurement = measurement_model_radar.function(state, noise=True)
measurements_radar.append(Detection(measurement, timestamp=state.timestamp))
# generate "AIS" measurements
measurements_AIS = []
for state in truth:
measurement = measurement_model_ais.function(state, noise=True)
measurements_AIS.append(Detection(measurement, timestamp=state.timestamp))
# save the ground truth and the measurements for the radar and the AIS
store_object.store_object(truth, "../scenarios/scenario2/ground_truth.pk1")
store_object.store_object(measurements_radar, "../scenarios/scenario2/measurements_radar.pk1")
store_object.store_object(measurements_AIS, "../scenarios/scenario2/measurements_ais.pk1")
store_object.store_object(start_time, "../scenarios/scenario2/start_time.pk1")
store_object.store_object(measurement_model_radar, "../scenarios/scenario2/measurement_model_radar.pk1")
store_object.store_object(measurement_model_ais, "../scenarios/scenario2/measurement_model_ais.pk1")