def generate_scenario():
time = np.arange(0, t_max, sample_time, dtype=float)
radar = autosim.SquareRadar(radar_range, clutter_density, PD,
measurement_covariance)
measurements_clutter = [
radar.generate_clutter_measurements(timestamp) for timestamp in time
]
target_measurements, true_target = generate_target(time, radar)
measurements_all = []
for k, measurement in enumerate(target_measurements):
measurements_all.append(measurements_clutter[k].union(measurement))
return true_target, measurements_all, time
def generate_scenario(targets=target_mmsi, tmax=540):
time = np.arange(0, tmax, 3, dtype=float)
radar = autosim.SquareRadar(radar_range, clutter_density, PD_high, measurement_covariance)
true_targets = dict()
true_detectability_mode = dict()
measurements_all = generate_clutter(time, radar)
for mmsi in targets:
target_measurements, target_state, detectability_state = generate_target(time, mmsi, radar)
true_targets[mmsi] = target_state
true_detectability_mode[mmsi] = detectability_state
for k, measurement in enumerate(target_measurements):
measurements_all[k] = measurements_all[k].union(measurement)
return true_targets, true_detectability_mode, measurements_all, time
def generate_scenario():
time = np.arange(0, t_max, sample_time, dtype=float)
radar = autosim.SquareRadar(radar_range, clutter_density, PD_high,
measurement_covariance_matrix)
measurements_clutter = generate_clutter(time, radar)
target_measurements, target_state, detectability_state, existence_state = generate_target(
time, radar)
true_target = target_state
true_detectability_mode = detectability_state
true_existence = existence_state
measurements_all = []
for k, measurement in enumerate(target_measurements):
measurements_all.append(measurements_clutter[k].union(measurement))
return true_target, true_detectability_mode, true_existence, measurements_clutter, measurements_all, time
def setup_radar():
radar_range = 500
clutter_density = 5e-5
high_clutter_density = 2e-4
P_D = 0.9
R = 7.**2 * np.identity(2)
radar = autosim.SquareRadar(radar_range,
clutter_density,
detection_probability=P_D,
measurement_covariance=R)
clutter_map = autoclutter.GeometricClutterMap(-radar_range, radar_range,
-radar_range, radar_range,
clutter_density)
high_density_region = autoclutter.SquareRegion(
high_clutter_density, [[-radar_range, -radar_range], [0, -radar_range],
[0, 0], [-radar_range, 0]])
clutter_map.add_region(high_density_region)
radar.add_clutter_map(clutter_map)
return radar
p11 = 0.98 # Survival probability p21 = 0 # Probability of birth P_Markov = np.array([[p11, 1 - p11], [p21, 1 - p21]]) initiate_thresh = 0.99 terminate_thresh = 0.10 # MofN N_test = 6 M_req = 4 N_terminate = 3 c1 = 15 c2 = 25 # Set up tracking system v_max = 10*dt radar = simulation.SquareRadar(radar_range, clutter_density, P_D, R) gate = tracking.TrackGate(P_G, v_max) target_model = tracking.DWNAModel(q) PDAF_tracker = tracking.PDAFTracker(P_D, target_model, gate) M_of_N = track_initiation.MOfNInitiation(M_req, N_test, PDAF_tracker, gate) N_timesteps = 20 grid_density = 50 # true_clutter_map = clutter_maps.GeometricClutterMap(-radar_range, radar_range, -radar_range, radar_range, clutter_density) # true_clutter_map = clutter_maps.nonuniform_musicki_map() # true_clutter_map = clutter_maps.nonuniform_test_map(radar_range) true_clutter_map = test_scenario.clutter_testing_map() classic_clutter_map = clutter_maps.ClassicClutterMap.from_geometric_map(true_clutter_map, grid_density, N_timesteps) spatial_clutter_map = clutter_maps.SpatialClutterMap.from_geometric_map(true_clutter_map, grid_density, N_timesteps) temporal_clutter_map = clutter_maps.TemporalClutterMap.from_geometric_map(true_clutter_map, grid_density, N_timesteps)
def false_tracks(P_D, target_model, gate, M_req, N_test, N_terminate,
initiate_thresh, terminate_thresh, P_Markov, radar_range, R,
time):
print('Starting false tracks analysis')
clutter_MofN = dict()
clutter_IPDA = dict()
clut_arr = [4e-5, 3.5e-5, 3e-5, 2.5e-5, 2e-5, 1.5e-5, 1e-5, 5e-6]
for method in range(2):
clut_it = -1
for run in range(len(clut_arr)):
# Run tracking
if method == 0:
PDAF_tracker = tracking.PDAFTracker(P_D, target_model, gate)
M_of_N = track_initiation.MOfNInitiation(
M_req, N_test, PDAF_tracker, gate)
track_termination = tracking.TrackTerminatorMofN(N_terminate)
track_manager = tracking.Manager(PDAF_tracker, M_of_N,
track_termination)
else:
IPDAF_tracker = tracking.IPDAFTracker(P_D, target_model, gate,
P_Markov, gate.gamma)
IPDAInitiation = track_initiation.IPDAInitiation(
initiate_thresh, terminate_thresh, IPDAF_tracker, gate)
track_termination = tracking.TrackTerminatorIPDA(
terminate_thresh)
track_manager = tracking.Manager(IPDAF_tracker, IPDAInitiation,
track_termination)
clut_it += 1
clutter_density = clut_arr[clut_it]
print(clutter_density)
radar = simulation.SquareRadar(radar_range, clutter_density, P_D,
R)
for k, timestamp in enumerate(time):
measurements = radar.generate_clutter_measurements(timestamp)
track_manager.step(measurements, timestamp)
if k % 50 == 0:
print(len(track_manager.track_file))
if method == 0:
clutter_MofN[clutter_density] = len(track_manager.track_file)
else:
clutter_IPDA[clutter_density] = len(track_manager.track_file)
list_MofN = sorted(clutter_MofN.items())
list_IPDA = sorted(clutter_IPDA.items())
xMofN, yMofN = zip(*list_MofN)
xIPDA, yIPDA = zip(*list_IPDA)
str_out = ('Densities IPDA: ' + str(xIPDA) + '\n\nFalse tracks IPDA: ' +
str(yIPDA) + '\n\nDensities MofN: ' + str(xMofN) +
'\n\nFalse tracks MofN: ' + str(yMofN))
with codecs.open('./Results/{}.txt'.format('false_tracks_sim'), 'wt',
'utf-8') as file:
file.write(str_out)
# Plot
fig, ax = visualization.setup_plot(None)
plt.semilogy(xMofN, yMofN, '--', label='M of N')
plt.semilogy(xIPDA, yIPDA, label='IPDA')
ax.set_title('False tracks detected over 1000 scans')
ax.set_xlabel('Clutter density')
ax.set_ylabel('False tracks detected')
plt.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax.legend()