def test_TrackerEvaluator(self):
eval = trackers.TrackerEvaluator(
tracker=trackers.GNN(sensor=sensors.BaseSensor(R=np.diag(
[0.001, 0.001]),
PD=0.7,
VC=1.0,
PFA=1e-6,
BNT=0.03,
y_mins=[-250, -250],
y_maxs=[+250, +250],
y_stps=[1, 1]),
model_factory=models.SimpleModelFactory(
model=models.KalmanModel, dT=1.0, q=1.0),
track_factory=tracks.BaseTrackFactory(
track=tracks.ScoreManagedTrack)),
tgt_list=[
models.SimpleTarget(SD=2,
x0=[0., 0., +0., +1.],
start_time=0.0),
models.SimpleTarget(SD=2,
x0=[0., 10., +1., -1.],
start_time=0.0),
models.SimpleTarget(SD=2,
x0=[10., 10., -1., -1.],
start_time=0.0),
models.SimpleTarget(SD=2,
x0=[10., 0., +0., +1.],
start_time=1.0)
])
# eval.plot_position()
eval.estimate_track_statistics()
def test_TrackEvaluator(self):
scan_time = 1.0
sigma_o = 1.0
time_m = 2.0
sigma_mx = 4.0
sigma_my = 1.0
sigma_vx = 18.0
sigma_vy = 4.0
vx0 = np.random.normal(0.0, sigma_vx)
vy0 = np.random.normal(0.0, sigma_vy)
eval = tracks.TrackEvaluator(
sensor=sensors.BaseSensor(
PD=0.7,
VC=1.0,
PFA=1e-6,
BNT=0.03
),
model_factory=models.SingerModelFactory(
model=models.KalmanModel,
dT=1.0,
tm=time_m,
sm=[sigma_mx, sigma_my],
SD=2,
P0=np.diag([sigma_o**2, sigma_o**2, sigma_vx**2, sigma_vy**2])
),
track_factory=tracks.BaseTrackFactory(
track=tracks.ScoreManagedTrack
),
target=models.SimpleTarget(
x0=[0.0, 0.0, vx0, vy0],
SD=2
),
R=np.diag([sigma_o**2, sigma_o**2])
)
# eval.plot_score()
def sample_MultiSensorGNN():
"""
This program calculate two target / two sensor case with GNN, and animate it.
"""
PD = 0.99
PFA = 1e-7
scan_time = 0.5
sigma_o = 1.0
time_m = 2.0
sigma_mx = 4.0
sigma_my = 1.0
sigma_vx = 18.0
sigma_vy = 4.0
class Radar2DSensor(sensors.Polar2DSensor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def is_trk_in_range(self, trk):
dist, azim = cmath.polar( (trk.model.x[0]-self.x[0]) + 1j*(trk.model.x[1]-self.x[1]) )
return self.range_min < dist < self.range_max and abs(azim-self.angle) < self.width/2
def is_tgt_in_range(self, tgt):
dist, azim = cmath.polar( (tgt.x[0]-self.x[0]) + 1j*(tgt.x[1]-self.x[1]) )
return self.range_min < dist < self.range_max and abs(azim-self.angle) < self.width/2
sen_list = [
Radar2DSensor(
PD=PD,
VC=1.0,
PFA=PFA,
BNT=0.03,
x0=np.array([0.0, 10.0, 1.0, 0.0]),
angle0=15/180*cmath.pi,
DETECT_RANGE_MAX=40,
DETECT_RANGE_MIN=10,
DETECT_WIDTH=120/180*cmath.pi
),
Radar2DSensor(
PD=PD,
VC=1.0,
PFA=PFA,
BNT=0.03,
x0=np.array([50.0, 10.0, 1.0, 0.0]),
angle0=-15/180*cmath.pi,
DETECT_RANGE_MAX=40,
DETECT_RANGE_MIN=10,
DETECT_WIDTH=120/180*cmath.pi
)
]
tgt_list = [
models.SimpleTarget(SD=2, x0=[100., 00.,-1.,+0.3]),
models.SimpleTarget(SD=2, x0=[100., 10.,-1.,-0.1]),
models.SimpleTarget(SD=2, x0=[100., 20.,-1.,-0.2]),
]
tracker = trackers.GNN(
sen_list=sen_list,
model_factory=models.SingerModelFactory(
model=models.KalmanModel,
dT=scan_time,
tm=time_m,
sm=[sigma_mx, sigma_my],
SD=2,
P0=np.diag([sigma_o**2, sigma_o**2, sigma_vx**2, sigma_vy**2])
),
track_factory=tracks.BaseTrackFactory(
track=tracks.ScoreManagedTrack
)
)
obs_df = pd.DataFrame()
tgt_df = pd.DataFrame()
trk_df = pd.DataFrame()
sen_df = pd.DataFrame()
for i_scan in range(100):
timestamp = pd.Timestamp(i_scan, unit="s")
if not i_scan % 5:
# scan by sensor0 (once in 5 times)
sensor = sen_list[0]
R = np.eye(2) * 0.01
obs_list = [models.Obs(np.random.multivariate_normal(tgt.x[:2], R), R) for tgt in tgt_list if sensor.is_tgt_in_range(tgt)]
trk_list = tracker.register_scan(obs_list, sensor=sensor)
else:
# scan by sensor1 (everytime except sensor0 turn)
sensor = sen_list[1]
R = np.eye(2) * 0.01
obs_list = [models.Obs(np.random.multivariate_normal(tgt.x[:2], R), R) for tgt in tgt_list if sensor.is_tgt_in_range(tgt)]
trk_list = tracker.register_scan(obs_list, sensor=sensor)
# tgt_list update
[ tgt.update(tracker._dT()) for tgt in tgt_list ]
# save as dataframe
obs_df = obs_df.append( [ obs.to_record(timestamp, i_scan, tracker.y_mdl_type()) for obs in obs_list ], ignore_index=True )
trk_df = trk_df.append( [ trk.to_record(timestamp, i_scan) for trk in trk_list ], ignore_index=True )
tgt_df = tgt_df.append( [ tgt.to_record(timestamp, i_scan) for tgt in tgt_list ], ignore_index=True)
sen_df = sen_df.append( [ sen.to_record(timestamp, i_scan) for sen in sen_list ], ignore_index=True )
# export
anal = analyzers.BaseAnalyzer.import_df(obs_df, trk_df, sen_df, tgt_df)
anal.export_csv()
anal.export_db()
# analyse
anal.animation()
def sample_FGT(is_fgt=True):
PD = 0.75
PFA = 1e-7
scan_time = 0.5
sigma_o = 1.0
time_m = 2.0
sigma_mx = 4.0
sigma_my = 1.0
sigma_vx = 18.0
sigma_vy = 4.0
np.random.seed(0)
class Radar2DSensor(sensors.Polar2DSensor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def is_trk_in_range(self, trk):
dist, azim = cmath.polar( (trk.model.x[0]-self.x[0]) + 1j*(trk.model.x[1]-self.x[1]) )
return self.range_min < dist < self.range_max and abs(azim-self.angle) < self.width/2
def is_tgt_in_range(self, tgt):
dist, azim = cmath.polar( (tgt.x[0]-self.x[0]) + 1j*(tgt.x[1]-self.x[1]) )
return self.range_min < dist < self.range_max and abs(azim-self.angle) < self.width/2
sen_list = [
Radar2DSensor(
PD=PD,
VC=1.0,
PFA=PFA,
BNT=0.03,
x0=np.array([0.0, 10.0, 0.0, 0.0]),
angle0=0.0/180*cmath.pi,
DETECT_RANGE_MAX=10000,
DETECT_RANGE_MIN=10,
DETECT_WIDTH=120/180*cmath.pi
)
]
tgt_list = [
models.SimpleTarget(SD=2, x0=[10000., 00.,-10.,+0.0]),
models.SimpleTarget(SD=2, x0=[10000., 10.,-10.,+0.0]),
models.SimpleTarget(SD=2, x0=[10000., 20.,-10.,-0.0]),
models.SimpleTarget(SD=2, x0=[10000., 30.,-10.,-0.0]),
models.SimpleTarget(SD=2, x0=[10000., 40.,-10.,-0.0]),
models.SimpleTarget(SD=2, x0=[10000., 50.,-10.,-0.0]),
models.SimpleTarget(SD=2, x0=[10000., 60.,-10.,-0.0]),
models.SimpleTarget(SD=2, x0=[10000., 70.,-10.,-0.0]),
models.SimpleTarget(SD=2, x0=[10000., 80.,-10.,-0.0]),
]
if is_fgt:
tracker = trackers.FGT(
sen_list=sen_list,
model_factory=models.SingerModelFactory(
model=models.KalmanModel,
dT=scan_time,
tm=time_m,
sm=[sigma_mx, sigma_my],
SD=2,
P0=np.diag([sigma_o**2, sigma_o**2, sigma_vx**2, sigma_vy**2])
),
track_factory=tracks.BaseTrackFactory(
track=tracks.FormationTrack
)
)
else:
tracker = trackers.GNN(
sen_list=sen_list,
model_factory=models.SingerModelFactory(
model=models.KalmanModel,
dT=scan_time,
tm=time_m,
sm=[sigma_mx, sigma_my],
SD=2,
P0=np.diag([sigma_o**2, sigma_o**2, sigma_vx**2, sigma_vy**2])
),
track_factory=tracks.BaseTrackFactory(
track=tracks.SimpleManagedTrack
)
)
obs_df = pd.DataFrame()
tgt_df = pd.DataFrame()
trk_df = pd.DataFrame()
sen_df = pd.DataFrame()
for i_scan in range(100):
timestamp = pd.Timestamp(i_scan, unit="s")
sensor = sen_list[0]
R = np.eye(2) * 20.0
obs_list = [
models.Obs(np.random.multivariate_normal(tgt.x[:2], R), R)for tgt in tgt_list
if sensor.is_tgt_in_range(tgt) and np.random.choice([True, False], p=[PD, 1-PD])
]
trk_list = tracker.register_scan(obs_list, sensor=sensor)
# tgt_list update
[ tgt.update(tracker._dT()) for tgt in tgt_list ]
# save as dataframe
obs_df = obs_df.append( [ obs.to_record(timestamp, i_scan, tracker.y_mdl_type()) for obs in obs_list ], ignore_index=True )
trk_df = trk_df.append( [ trk.to_record(timestamp, i_scan) for trk in trk_list ], ignore_index=True )
tgt_df = tgt_df.append( [ tgt.to_record(timestamp, i_scan) for tgt in tgt_list ], ignore_index=True)
sen_df = sen_df.append( [ sen.to_record(timestamp, i_scan) for sen in sen_list ], ignore_index=True )
# export
anal = analyzers.BaseAnalyzer.import_df(obs_df, trk_df, sen_df, tgt_df)
if is_fgt:
anal.export_db(fpath="data_fgt")
else:
anal.export_db(fpath="data_gnn")
# analyze
anal.plot2D()
def generate_irst_example_p878(PD=0.7, PFA=1e-6, tracker_type="GNN"):
""" IRST example of p.878
unit is pixcel (=70urad)
ref) Design and Analysis of Modern Tracking Systems
13.3.5 IRST Example
"""
scan_time = 1.0
sigma_o = 1.0
time_m = 2.0
sigma_mx = 4.0
sigma_my = 1.0
sigma_vx = 18.0
sigma_vy = 4.0
vx0 = np.random.normal(0.0, sigma_vx)
vy0 = np.random.normal(0.0, sigma_vy)
if tracker_type == "JPDA":
tracker = trackers.JPDA
model = models.PDAKalmanModel
track = tracks.PDATrack
else:
tracker = trackers.GNN
model = models.KalmanModel
track = tracks.ScoreManagedTrack
ret = trackers.TrackerEvaluator(
tracker=tracker(
sensor=sensors.BaseSensor(
R=np.diag([sigma_o**2, sigma_o**2]),
PD=PD,
VC=1.0,
PFA=PFA,
BNT=0.03,
# y_mins=[-44880,-250],
# y_maxs=[+44880,+250],
y_mins=[-1125, -250], # reduce calculation load
y_maxs=[+1125, +250], # reduce calculation load
y_stps=[1, 1]),
model_factory=models.SingerModelFactory(
model=model,
dT=scan_time,
tm=time_m,
sm=[sigma_mx, sigma_my],
SD=2,
P0=np.diag([sigma_o**2, sigma_o**2, sigma_vx**2,
sigma_vy**2])),
track_factory=tracks.BaseTrackFactory(track=track)),
tgt_list=[
models.SimpleTarget(x0=[0.0, 0.0, vx0, vy0], SD=2),
],
R=np.diag([sigma_o**2, sigma_o**2]))
# TODO: implement SPRT, SMC(MHT), FMC(MHT), FMC(IPDA)
# SPRT( Sequential Probability Ratio Test )
# 13.3.3 SPRT Analysis of Track Confirmation (p874)
# SMC ( Simplified Monte Carlo Simulation )
# 13.3.4 Simplified Monte Carlo Simulation (p877)
# FMC ( Full Monte Carlo Simulation ) (p878)
# 13.3.5 IRST Example
return ret