def aer_obs(self, obs):
i = self.i
for j in range(self.m):
obs[4*j: 4*j + 3] = hx(self.x_filter[i, j, :3], self.trans_matrix[i], self.obs_lla, self.obs_itrs)
obs[4*j + 3] = np.trace(self.P_filter[i, j])
obs = np.nan_to_num(obs, copy=False, nan=0.001, posinf=0.001, neginf=0.001)
return obs
def aer_true_all(self):
s = time.time()
aer = []
for i in range(self.n):
for j in range(self.m):
aer.append(hx(self.x_true[i, j, :3], self.trans_matrix[i], self.obs_lla, self.obs_itrs))
aer = np.array(aer)
aer = np.reshape(aer, (self.n, self.m, 3))
e = time.time()
self.runtime['all_true_obs'] += e - s
return aer
candidates = []
seed = 0
random_state = np.random.RandomState(seed)
for i in tqdm(range(samples)):
regime = random_state.choice(a=['LEO', 'MEO', 'GEO', 'Tundra', 'Molniya'],
p=[1 / 3, 1 / 3, 1 / 9, 1 / 9, 1 / 9])
accepted = False
while not accepted:
candidate = init_state_vec(orbits=[regime], random_state=random_state)
x_gcrs = [fx(candidate, step_size * i) for i in range(n)]
x_itrs = [x_gcrs[i][:3] @ trans_matrix[i] for i in range(n)]
x_lla = np.array([ecef2lla(x_itrs[i]) for i in range(n)])
candidate_elevation = [
hx(x_gcrs=x_gcrs[i][:3], **hx_kwargs[i])[1] for i in range(n)
]
visibility = candidate_elevation >= obs_limit
gaps = [
sum(1 for _ in group)
for key, group in itertools.groupby((visibility - 1) * -1) if key
]
if np.all(x_lla[:, 2] > 300 * 1000):
if not gaps == []:
if sum(visibility[0:int(first_window * 60 / step_size)]) > 0:
if np.max(gaps) < max_gap * 60 * 60 / step_size:
candidates.append(candidate)
accepted = True
else:
if np.all(visibility):
candidates.append(candidate)
])
observer_lat = np.radians(38.828198)
observer_lon = np.radians(-77.305352)
observer_alt = np.float64(20.0) # in meters
observer_lla = np.array([observer_lat, observer_lon, observer_alt])
observer_itrs = lla2ecef(observer_lla) # meter
t = datetime(year=2007, month=4, day=5, hour=12, minute=0, second=0)
trans_matrix = gcrs2irts_matrix_b(t, eop)
hx_args1 = (trans_matrix, observer_lla, observer_itrs)
hx_args2 = (t, observer_lla, None, observer_itrs)
z1 = hx(x, *hx_args1)
z2 = hx2(x, *hx_args2)
hx_error = z2 - z1
print("Test 10a: hx error in azimuth (arc seconds) = ",
np.degrees(hx_error[0]) * 60 * 60)
print("Test 10b: hx error in elevation (arc seconds) = ",
np.degrees(hx_error[1]) * 60 * 60)
print("Test 10c: hx error in slant range (meters) = ", hx_error[2])
# resolved Issue opened with AstroPy at https://github.com/astropy/astropy/issues/10407
# !------------ Test 11 - Az El Updates
from filterpy.kalman import MerweScaledSigmaPoints as MerweScaledSigmaPoints
from filterpy.kalman.UKF import UnscentedKalmanFilter as UnscentedKalmanFilter