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femtoLocalization.py
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femtoLocalization.py
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"""
Pypredict
Orbit prediction software. Displays the satellites' position and
orbital parameters in real time. Simulates satellite localization
and deployment.
Copyright (C) 2018-2021, Matías Vidal Valladares, matvidal.
Authors: Matías Vidal Valladares <[email protected]>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
from AOAwithTDOA import Locate
from pypredict.dpl import Dpl
from pypredict.ekf import EKF
from pypredict.sat import Sat
from pypredict.localizationSystem import Loc
from numpy import arange, arctan2, arccos, array, cos, linalg, matrix, mean, pi, random, savetxt, sin, std, sqrt, zeros, transpose
from matplotlib.pyplot import axis, figure, plot, savefig, setp, show, subplots, subplots_adjust, tight_layout, xticks
from datetime import datetime, timedelta
from pkg_resources import resource_filename
import csv
class Loc(object):
def __init__(self):
self.ekf = EKF()
self.loc = Locate()
self.z = matrix([[0.0], [0.0], [0.0], [0.0], [0.0], [0.0]])
self.Q_ip = matrix([[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]])
self.Q_po = matrix([[0.0, 0.0, 0.0],
[0.0, 0.0, -1.0],
[0.0, 0.0, 0.0]])
self.dpl = Dpl()
#self.test_case()
def __call__(self):
return self
def __str__(self):
return "This localization system combines a model of an orbit obtained at deploy time with TDOA and AOA measurements. This is done by using an Extended Kalman Filter (EKF)."
def initTransformationData(self, i, RAAN, theta, w, r):
"""
Initializes the rotation matrices and a translation vector.
Parameters
----------
i : float
The inclination of the main satellite, the one which is
at the center of the orbital frame, in radians.
RAAN : float
The right ascention of the ascending node of the
main satellite in radians.
theta : float
The true anomaly of the main satellite in radians.
w : float
The argument of the perigee of the main satellite in
radians.
r : matrix
The position vector of the main satellite in meters.
"""
sin_RAAN = sin(RAAN)
cos_RAAN = cos(RAAN)
sin_i = sin(i)
cos_i = cos(i)
sin_w = sin(w)
cos_w = cos(w)
#self.Q_ip = matrix([[-sin_RAAN*cos_i*sin_w + cos_RAAN*cos_w,
# cos_RAAN*cos_i*sin_w + sin_RAAN*cos_w,
# sin_i*sin_w],
# [-sin_RAAN*cos_i*cos_w - cos_RAAN*sin_w,
# cos_RAAN*cos_i*cos_w - sin_RAAN*sin_w,
# sin_i*cos_w],
# [sin_RAAN*sin_i, -cos_RAAN*sin_i, cos_i]])
#print("A: {}\n".format(self.Q_ip))
self.Q_ip[0,0] = -sin_RAAN*cos_i*sin_w + cos_RAAN*cos_w
self.Q_ip[0,1] = cos_RAAN*cos_i*sin_w + sin_RAAN*cos_w
self.Q_ip[0,2] = sin_i*sin_w
self.Q_ip[1,0] = -sin_RAAN*cos_i*cos_w - cos_RAAN*sin_w
self.Q_ip[1,1] = cos_RAAN*cos_i*cos_w - sin_RAAN*sin_w
self.Q_ip[1,2] = sin_i*cos_w
self.Q_ip[2,0] = sin_RAAN*sin_i
self.Q_ip[2,1] = -cos_RAAN*sin_i
self.Q_ip[2,2] = cos_i
#print("B: {}\n".format(self.Q_ip))
phi = theta + pi*0.5
cos_phi = cos(phi)
sin_phi = sin(phi)
#self.Q_po = matrix([[cos_phi, sin_phi, 0],
# [0, 0, -1],
# [-sin_phi, cos_phi, 0]])
self.Q_po[0,0] = cos_phi
self.Q_po[0,1] = sin_phi
self.Q_po[2,0] = -sin_phi
self.Q_po[2,1] = cos_phi
self.Q_poQ_ip = self.Q_po*self.Q_ip
self.translation = self.Q_poQ_ip*r
def transformPosition(self, r):
"""
Transforms the position vector r from the inertial frame to
the perifocal frame, and then from the perifocal frame to the
orbital frame.
Parameters
----------
r: matrix
Position vector in the inertial frame.
"""
return self.Q_poQ_ip*r - self.translation
def transformVelocity(self, v):
"""
Transforms the velocity from the inertial frame to the
perifocal frame, and then from the perifocal frame to the
orbital frame.
Parameters
----------
v: matrix
Velocity in the inertial frame.
"""
v = self.Q_poQ_ip*v
return v - self.mainSat_v
def orbital2inertial(self, r):
"""
Transforms the vector r from the orbital frame to the
perifocal frame, and then from the perifocal frame to the
inertial frame.
Parameters
----------
r: matrix
Position vector in the orbital frame.
"""
#r_in = transpose(self.Q_ip)*transpose(self.Q_po)*(r + self.translation)
r_in = (self.Q_po*self.Q_ip).T*(r + self.translation)
return r_in
def generate_noise(self, std_RD, std_AOA, std_GNSS, L):
e = self.loc.get_error_vector(std_RD, std_AOA, L)
GNSS_noise = self.loc.get_GNSS_noise(std_GNSS, L)
return e, GNSS_noise
def estimate_before_ekf(self, e, GNSS_noise, s1, s2, u, Q):
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(s1, s2, GNSS_noise)
k_w_GNSS_error = self.loc.get_real_vector(u, noisy_s1, noisy_s2)
u_hat = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e, Q)
return noisy_s1, noisy_s2, u_hat
def estimate_with_ekf(self, v, dt, n, rc, x, s1, s2, z):
self.mainSat_v = self.Q_poQ_ip*v
#x[0:3] = self.transformPosition(x[0:3])
#x[3:6] = self.transformVelocity(x[3:6])
lvlh_s1 = self.transformPosition(s1)
lvlh_s2 = self.transformPosition(s2)
lvlh_z = self.transformPosition(z)
#print("z: {}".format(lvlh_z))
self.ekf.newIteration(dt, n, rc, x, lvlh_s1, lvlh_s2, lvlh_z)
def getEstimatedPos(self):
"""
Returns the estimated position vector in the inertial frame.
"""
r_orb = self.ekf.getPosition()
return self.orbital2inertial(r_orb)
def getEstimatedVel(self):
"""
Returns the estimated velocity vector in the inertial frame.
"""
v_orb = self.ekf.getVelocity() + self.mainSat_v
#v_in = transpose(self.Q_ip)*transpose(self.Q_po)*v_orb
v_in = (self.Q_po*self.Q_ip).T*v_orb
return v_in
def test_case(self):
start = datetime.utcnow()
date0 = datetime(2020, 11, 17, 00, 13, 33, 0)
dep_date = date0 + timedelta(minutes=10)
x = matrix([[0], [0], [0], [0], [0], [0]])
std_RD = 10.0
std_AOA = 1.0*pi/180.0
std_GNSS = 10.0
Q = self.loc.get_Q(std_RD, std_AOA)
L = 2#0#00 #Con 50 -> +9 hrs.
L0 = 2#000
minutes = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]
acum_ekf = zeros((len(minutes), 3, 1))
real_positions = zeros((len(minutes), 3, 1))
rmse = zeros(len(minutes))
bias = zeros(len(minutes))
meas_error = zeros(len(minutes))
div = 32
finer_minutes = arange(0, div*100+1, dtype="float")/div
dt = 2#5
ekf_time = arange(0, int(100*60/dt)+1, dtype="float")
rcrb = zeros(len(finer_minutes))
ekf_error = zeros(len(ekf_time))
dist_u_s1 = zeros(len(finer_minutes))
dist_u_s2 = zeros(len(finer_minutes))
alpha_max = zeros(len(finer_minutes))
alpha_mean = zeros(len(finer_minutes))
alpha_min = zeros(len(finer_minutes)) + 180
data_path = resource_filename("pypredict","data/")
sat_u = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s1 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2.setTLE("1 44814U 19081L 20321.73053029 .00001305 00000-0 63025-4 0 9996",
"2 44814 97.4788 21.6285 0013387 80.2501 280.0246 15.20374749 54001")
with open("u_TLEs.txt", 'r') as u_TLEs:
sat_u_TLEs = u_TLEs.readlines()
with open("s1_TLEs.txt", 'r') as s1_TLEs:
sat_s1_TLEs = s1_TLEs.readlines()
for i in range(L0):
print("Deployment {}/{}".format(i+1, L0))
#studied_date = dep_date + timedelta(days=3)
#j = 0
#e, GNSS_noise = self.generate_noise(std_RD, std_AOA, std_GNSS, L)
sat_u.setTLE(sat_u_TLEs[1 + i*3], sat_u_TLEs[2 + i*3])
sat_s1.setTLE(sat_s1_TLEs[1 + i*3], sat_s1_TLEs[2 + i*3])
acum_ekf = acum_ekf*0
self.ekf.initMatrices()
#sat_u.updateOrbitalParameters(studied_date)
#sat_s1.updateOrbitalParameters(studied_date)
#sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
#u = sat_u.getXYZ()
#s1 = sat_s1.getXYZ()
#s2 = sat_s2.getXYZ()
#k = self.loc.get_real_vector(u, s1, s2)
#MSE = self.loc.get_MSE(u, s1, s2, k, Q)
#rcrb[j] += MSE[0,0] + MSE[1,1] + MSE[2,2]
#noisy_s1, noisy_s2, u_hat = self.estimate_before_ekf(e[:,j], GNSS_noise[:,j], s1, s2, u, Q)
#self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, s1)
#x[0:3] = self.transformPosition(s1)#self.transformPosition(u_hat.copy()) #noisy_s1
#x[3:6] = 0 #sat_s1self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1).getInertialVel()
#meas_error[0] += linalg.norm(u - u_hat)
#ekf_error[0] += linalg.norm(u - u_hat)
#print("Measurement error: {}".format(meas_error[0]))
for j in range(L):
studied_date = dep_date + timedelta(days=3)
sat_s1.updateOrbitalParameters(studied_date - timedelta(seconds=dt))
s1 = sat_s1.getXYZ().copy()
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, s1)
x[0:3] = self.transformPosition(s1)#self.transformPosition(u_hat.copy()) #noisy_s1
x[3:6] = 0 #sat_s1.getInertialVel()
rc = sat_s1.a*sqrt(1 - sat_s1.e**2)
self.ekf.Predict(dt, sat_s1.n, rc, x)
x = self.ekf.x_pred.copy()
e, GNSS_noise = self.generate_noise(std_RD, std_AOA, std_GNSS, len(minutes))
for t in range(int(100*60/dt)+1):
#studied_date += timedelta(seconds=dt)
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
s1 = sat_s1.getXYZ().copy()
u = sat_u.getXYZ().copy()
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, s1)
lvlh_u = self.transformPosition(u).copy()
rc = sat_s1.a*sqrt(1 - sat_s1.e**2)
#print(rc)
#self.ekf.Predict(dt, sat_s1.n, rc, x)
#x = self.ekf.x_pred.copy()
#if (j == 0):
#sat_u.updateOrbitalParameters(studied_date)
#sat_s1.updateOrbitalParameters(studied_date)
#sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
#u = sat_u.getXYZ()
#s1 = sat_s1.getXYZ()
#s2 = sat_s2.getXYZ()
#k = self.loc.get_real_vector(u, s1, s2)
#MSE = self.loc.get_MSE(u, s1, s2, k, Q)
#rcrb[t] += MSE[0,0] + MSE[1,1] + MSE[2,2]
if (t%(300/dt) == 0):
#ekf_estimations = []
#acumulated_meas = 0
#acumulated_ekf = 0
#sat_u.updateOrbitalParameters(studied_date)
#sat_s1.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
#u = sat_u.getXYZ()
#s1 = sat_s1.getXYZ().copy()
s2 = sat_s2.getXYZ().copy()
index = int(t/(300/dt))
#rc = sat_s1.a*sqrt(1 - sat_s1.e**2)
noisy_s1, noisy_s2, u_hat = self.estimate_before_ekf(e[:,index], GNSS_noise[:,index], s1, s2, u, Q)
#self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1)
#self.estimate_with_ekf(sat_s1.getInertialVel(), dt, sat_s1.n, rc, x, noisy_s1, noisy_s2, u_hat)
#self.mainSat_v = self.Q_poQ_ip*sat_s1.getInertialVel()
lvlh_s1 = self.transformPosition(noisy_s1)
lvlh_s2 = self.transformPosition(noisy_s2)
lvlh_z = self.transformPosition(u_hat)
self.ekf.Update(lvlh_s1, lvlh_s2, lvlh_z)
#acumulated_meas += linalg.norm(u - u_hat)
#print("Measurement error: {}".format(meas_error[j]))
#acumulated_ekf += linalg.norm(self.transformPosition(u) - self.ekf.x_up[0:3])
#print("{}.- EKF error: {}".format(j, ekf_error[j-1]))
x = self.ekf.x_up.copy()
#ekf_estimations.append(x[0:3])
#lvlh_u = self.transformPosition(u)
meas_error[index] += linalg.norm(u - u_hat)
#ekf_error[index] += linalg.norm(lvlh_u - x[0:3])
rmse[index] += linalg.norm(lvlh_u - x[0:3])**2
acum_ekf[index] += x[0:3]
if (j == 0):
real_positions[index] = lvlh_u
#self.loc.RMSE(self.transformPosition(u), ekf_estimations, L)
#bias[int(t/30)] += self.loc.Bias(self.transformPosition(u), ekf_estimations, L)
#print("RMSE: {}, Bias: {}".format(rmse[int(j/30)], bias[int(j/30)]))
#x = self.ekf.x_up.copy()
ekf_error[t] += linalg.norm(lvlh_u - x[0:3])
studied_date += timedelta(seconds=dt)
self.ekf.Predict(dt, sat_s1.n, rc, x)
x = self.ekf.x_pred.copy()
#meas_error[int(t/30)] += acumulated_meas/L
#ekf_error[int(t/30)] += acumulated_ekf/L
#rmse[int(t/30)] += self.loc.RMSE(self.transformPosition(u), ekf_estimations, L)
#bias[int(t/30)] += self.loc.Bias(self.transformPosition(u), ekf_estimations, L)
for en, acum in enumerate(acum_ekf): bias[en] += linalg.norm(acum/L - real_positions[en])**2
#print(bias)
meas_error = meas_error/L0/L
ekf_error = ekf_error/L0/L
rmse = sqrt(rmse/L0/L)
bias = sqrt(bias/L0)
with open("rcrb.csv", newline='') as f:
reader = csv.reader(f, delimiter=",")
next(reader)
data = array(list(reader))#[:,0]
for i in range(len(data)):
rcrb[i] = float(data[i,0])
#rcrb = sqrt(rcrb/L0)
fig, ax = subplots(1, 1, sharey=False)
ax.semilogy(minutes, rmse, 'o', linewidth=2.0, markersize=12,# clip_on=False,
fillstyle="none", label="RMSE")
ax.semilogy(finer_minutes, rcrb, '-', linewidth=2.0, markersize=12,
label="Root CRB", color="tab:orange", alpha=0.7)
ax.semilogy(minutes, bias, '+', linewidth=2.0, markersize=12,# clip_on=False,
label="Bias", color="tab:green")
ax.semilogy(minutes, meas_error, '-', linewidth=1.8, markersize=12,
label="Measurement error", color="tab:purple")
ax.semilogy(ekf_time*dt/60, ekf_error, '-', linewidth=1.8, markersize=12,
label="EKF error", color="tab:red")
ax.grid()
ax.legend(fontsize=14, loc="upper left")
ax.xaxis.label.set_size(16)
ax.yaxis.label.set_size(16)
tight_layout()
finish = datetime.utcnow()
print("Elapsed time: {}".format(finish-start))
show()
def test_case2(self):
std_RD = 10.0
std_AOA = 1.0*pi/180.0
std_GNSS = 10.0
Q = self.loc.get_Q(std_RD, std_AOA)
e_rcrb = zeros(42)
bias = zeros(42)
acum_est = zeros([42, 3, 1])
real_u = zeros([42, 3, 1])
real_s1 = zeros([42, 3, 1])
real_s2 = zeros([42, 3, 1])
data_path = resource_filename("pypredict","data/")
sat_u = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s1 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2.setTLE("1 44814U 19081L 20321.73053029 .00001305 00000-0 63025-4 0 9996",
"2 44814 97.4788 21.6285 0013387 80.2501 280.0246 15.20374749 54001")
with open("u_TLEs.txt", 'r') as u_TLEs:
sat_u_TLEs = u_TLEs.readlines()
with open("s1_TLEs.txt", 'r') as s1_TLEs:
sat_s1_TLEs = s1_TLEs.readlines()
date0 = datetime(2020, 11, 17, 00, 13, 33, 0)
dep_date = date0 + timedelta(minutes=10)
initial_date = dep_date + timedelta(days=3)
L0 = [3, 15, 76, 309, 425] # Worst cases
ekf = EKF()
meas_dt = 2.0#0.2
model_dt = 0.1
x = zeros([6,1])
iterations = 1
h = model_dt/iterations
start = datetime.utcnow()
minutes = zeros(42)
for i in range(21):
minutes[2*i] = i*5.0 - meas_dt/60
minutes[2*i + 1] = i*5.0
rmse = zeros(int(100*60/model_dt + 2))
L = 50#0#5000
for l0 in L0:
print("Despliegue: {}".format(l0))
sat_u.setTLE(sat_u_TLEs[1 + l0*3], sat_u_TLEs[2 + l0*3])
sat_s1.setTLE(sat_s1_TLEs[1 + l0*3], sat_s1_TLEs[2 + l0*3])
acum_est = acum_est*0
for l in range(L):
studied_date = initial_date
studied_date -= timedelta(seconds=2*meas_dt)
d = 0
e, GNSS_noise = self.generate_noise(std_RD, std_AOA, std_GNSS, int(6000/model_dt + 2))
for t in range(21):
studied_date += timedelta(seconds=meas_dt)
if (l==0):
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
real_u[2*t] = sat_u.getXYZ().copy()
real_s1[2*t] = sat_s1.getXYZ().copy()
real_s2[2*t] = sat_s2.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(real_s1[2*t], real_s2[2*t], GNSS_noise[:,d])
k_w_GNSS_error = self.loc.get_real_vector(real_u[2*t], noisy_s1, noisy_s2)
u_hat1 = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e[:,d], Q)
acum_est[2*t] += u_hat1
MSE = self.loc.get_MSE(u_hat1, noisy_s1, noisy_s2, k_w_GNSS_error, Q)
e_rcrb[2*t] += MSE[0,0] + MSE[1,1] + MSE[2,2]
rmse[d] += (u_hat1[0,0] - real_u[2*t,0,0])**2 + (u_hat1[1,0] - real_u[2*t,1,0])**2 + (u_hat1[2,0] - real_u[2*t,2,0])**2
d += 1
studied_date += timedelta(seconds=meas_dt)
sat_s1.updateOrbitalParameters(studied_date)
if (l==0):
sat_u.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
real_u[2*t+1] = sat_u.getXYZ().copy()
real_s1[2*t+1] = sat_s1.getXYZ().copy()
real_s2[2*t+1] = sat_s2.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(real_s1[2*t+1], real_s2[2*t+1], GNSS_noise[:,d])
k_w_GNSS_error = self.loc.get_real_vector(real_u[2*t+1], noisy_s1, noisy_s2)
u_hat2 = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e[:,d], Q)
acum_est[2*t+1] += u_hat2
MSE = self.loc.get_MSE(u_hat2, noisy_s1, noisy_s2, k_w_GNSS_error, Q)
e_rcrb[2*t+1] += MSE[0,0] + MSE[1,1] + MSE[2,2]
vel = (u_hat2 - u_hat1)/meas_dt
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1)
self.mainSat_v = self.Q_poQ_ip*sat_s1.getInertialVel().copy()
lvlh_u = self.transformPosition(u_hat2)
lvlh_v = self.transformVelocity(vel)
x[0:3] = lvlh_u.copy()
x[3:6] = lvlh_v.copy()
rmse[d] += (u_hat2[0,0] - real_u[2*t+1,0,0])**2 + (u_hat2[1,0] - real_u[2*t+1,1,0])**2 + (u_hat2[2,0] - real_u[2*t+1,2,0])**2
d += 1
rc = sat_s1.a*sqrt(1 - sat_s1.e**2)
sat_s1_n = sat_s1.n
if ((studied_date - initial_date).total_seconds() < 6000):
for i in range(int(300/model_dt)-2):#161):
studied_date += timedelta(seconds=model_dt)
model_pos = ekf.RK4(x, h, iterations, sat_s1_n, rc)
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
s1 = sat_s1.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(s1, real_s2[2*t+1], GNSS_noise[:,d])
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1)
real_pos = self.transformPosition(sat_u.getXYZ()).copy()
rmse[d] += (model_pos[0,0] - real_pos[0,0])**2 + (model_pos[1,0] - real_pos[1,0])**2 + (model_pos[2,0] - real_pos[2,0])**2
d += 1
x = model_pos.copy()
studied_date += timedelta(seconds=(model_dt - meas_dt)*2)
acum_est = acum_est/L
for j, u in enumerate(real_u):
bias[j] += (acum_est[j,0,0] - u[0,0])**2 + (acum_est[j,1,0] - u[1,0])**2 + (acum_est[j,2,0] - u[2,0])**2
print("Time3: {}".format((studied_date - initial_date).total_seconds()))
e_rcrb = sqrt(e_rcrb/len(L0)/L)
rmse = sqrt(rmse/len(L0)/L)
bias = sqrt(bias/len(L0))
with open("e_rcrb.csv", 'w') as data_file_rcrb:
outRCRB = csv.writer(data_file_rcrb)
outRCRB.writerow(["RMSE", "e RCRB", "Bias"])
for i in range(len(e_rcrb)):
outRCRB.writerow([rmse[i], e_rcrb[i], bias[i]])
for i in range(len(e_rcrb), len(rmse)):
outRCRB.writerow([rmse[i]])
finish = datetime.utcnow()
print("Start: {}\nFinish: {}\nDelta: {} minutes".format(start, finish, (finish-start).total_seconds()/60))
def test_case3(self, L0=[582, 2989, 963, 3714, 425], L=5000, output="rmse.csv"):
std_RD = 10.0
std_AOA = 1.0*pi/180.0
std_GNSS = 10.0
Q = self.loc.get_Q(std_RD, std_AOA)
data_path = resource_filename("pypredict","data/")
sat_u = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s1 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2.setTLE("1 44814U 19081L 20321.73053029 .00001305 00000-0 63025-4 0 9996",
"2 44814 97.4788 21.6285 0013387 80.2501 280.0246 15.20374749 54001")
with open("u_TLEs.txt", 'r') as u_TLEs:
sat_u_TLEs = u_TLEs.readlines()
with open("s1_TLEs.txt", 'r') as s1_TLEs:
sat_s1_TLEs = s1_TLEs.readlines()
date0 = datetime(2020, 11, 17, 00, 13, 33, 0)
dep_date = date0 + timedelta(minutes=10)
initial_date = dep_date + timedelta(days=3)
ekf = EKF()
meas_dt = 2.0#0.2
model_dt = 0.01
x = zeros([6,1])
iterations = 1
h = model_dt/iterations
start = datetime.utcnow()
minutes = zeros(26)
for i in range(13):
minutes[2*i] = (i*300.0 - meas_dt + 600.0)/60.0
minutes[2*i + 1] = (i*300.0 + 600.0)/60
e_rcrb = zeros(len(minutes))
bias = zeros(len(minutes))
rmse = zeros(len(minutes))
acum_est = zeros([len(minutes), 3, 1])
real_u = zeros([len(minutes), 3, 1])
real_s1 = zeros([len(minutes), 3, 1])
real_s2 = zeros([len(minutes), 3, 1])
model_rmse = zeros(2*int(300/model_dt) - 2)
for l0 in L0:
print("Despliegue: {}".format(l0))
sat_u.setTLE(sat_u_TLEs[1 + l0*3], sat_u_TLEs[2 + l0*3])
sat_s1.setTLE(sat_s1_TLEs[1 + l0*3], sat_s1_TLEs[2 + l0*3])
acum_est = acum_est*0
for l in range(L):
studied_date = initial_date + timedelta(minutes=10)
studied_date -= timedelta(seconds=2*meas_dt)
d = 0
e, GNSS_noise = self.generate_noise(std_RD, std_AOA, std_GNSS, int(6000/model_dt + 2))
for t in range(13):
studied_date += timedelta(seconds=meas_dt)
if (l==0):
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
real_u[2*t] = sat_u.getXYZ().copy()
real_s1[2*t] = sat_s1.getXYZ().copy()
real_s2[2*t] = sat_s2.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(real_s1[2*t], real_s2[2*t], GNSS_noise[:,d])
k_w_GNSS_error = self.loc.get_real_vector(real_u[2*t], noisy_s1, noisy_s2)
u_hat1 = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e[:,d], Q)
acum_est[2*t] += u_hat1
MSE = self.loc.get_MSE(u_hat1, noisy_s1, noisy_s2, k_w_GNSS_error, Q)
e_rcrb[2*t] += MSE[0,0] + MSE[1,1] + MSE[2,2]
rmse[2*t] += (u_hat1[0,0] - real_u[2*t,0,0])**2 + (u_hat1[1,0] - real_u[2*t,1,0])**2 + (u_hat1[2,0] - real_u[2*t,2,0])**2
d += 1
studied_date += timedelta(seconds=meas_dt)
sat_s1.updateOrbitalParameters(studied_date)
if (l==0):
sat_u.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
real_u[2*t+1] = sat_u.getXYZ().copy()
real_s1[2*t+1] = sat_s1.getXYZ().copy()
real_s2[2*t+1] = sat_s2.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(real_s1[2*t+1], real_s2[2*t+1], GNSS_noise[:,d])
k_w_GNSS_error = self.loc.get_real_vector(real_u[2*t+1], noisy_s1, noisy_s2)
u_hat2 = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e[:,d], Q)
acum_est[2*t+1] += u_hat2
MSE = self.loc.get_MSE(u_hat2, noisy_s1, noisy_s2, k_w_GNSS_error, Q)
e_rcrb[2*t+1] += MSE[0,0] + MSE[1,1] + MSE[2,2]
vel = (u_hat2 - u_hat1)/meas_dt
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1)
self.mainSat_v = self.Q_poQ_ip*sat_s1.getInertialVel().copy()
lvlh_u = self.transformPosition(u_hat2)
lvlh_v = self.transformVelocity(vel)
x[0:3] = lvlh_u.copy()
x[3:6] = lvlh_v.copy()
rmse[2*t+1] += (u_hat2[0,0] - real_u[2*t+1,0,0])**2 + (u_hat2[1,0] - real_u[2*t+1,1,0])**2 + (u_hat2[2,0] - real_u[2*t+1,2,0])**2
d += 1
seconds = (studied_date - initial_date).total_seconds()
date_before_model = studied_date
if (seconds == 2100):
rc = sat_s1.a*sqrt(1 - sat_s1.e**2)
sat_s1_n = sat_s1.n
for i in range(2*int(300/model_dt)-2):#161):
studied_date += timedelta(seconds=model_dt)
model_pos = ekf.RK4(x, h, iterations, sat_s1_n, rc)
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
s1 = sat_s1.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(s1, real_s2[2*t+1], GNSS_noise[:,d])
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1)
real_pos = self.transformPosition(sat_u.getXYZ()).copy()
model_rmse[i] += (model_pos[0,0] - real_pos[0,0])**2 + (model_pos[1,0] - real_pos[1,0])**2 + (model_pos[2,0] - real_pos[2,0])**2
d += 1
x = model_pos.copy()
studied_date = date_before_model + timedelta(seconds=300-2*meas_dt)
else:
studied_date += timedelta(seconds=300-2*meas_dt)
acum_est = acum_est/L
for j, u in enumerate(real_u):
bias[j] += (acum_est[j,0,0] - u[0,0])**2 + (acum_est[j,1,0] - u[1,0])**2 + (acum_est[j,2,0] - u[2,0])**2
print("Time3: {}".format((studied_date - initial_date).total_seconds()))
e_rcrb = sqrt(e_rcrb/len(L0)/L)
rmse = sqrt(rmse/len(L0)/L)
model_rmse = sqrt(model_rmse/len(L0)/L)
bias = sqrt(bias/len(L0))
with open(output, 'w') as data_file_rcrb:
outRCRB = csv.writer(data_file_rcrb)
outRCRB.writerow(["Model RMSE", "e RCRB", "Bias", "RMSE"])
for i in range(len(e_rcrb)):
outRCRB.writerow([model_rmse[i], e_rcrb[i], bias[i], rmse[i]])
for i in range(len(e_rcrb), len(model_rmse)):
outRCRB.writerow([model_rmse[i]])
finish = datetime.utcnow()
print("Start: {}\nFinish: {}\nDelta: {} minutes".format(start, finish, (finish-start).total_seconds()/60))
def test_without_model(self, L0=[2824], L=5000, output="best_rmse.csv"):
std_RD = 10.0
std_AOA = 1.0*pi/180.0
std_GNSS = 10.0
Q = self.loc.get_Q(std_RD, std_AOA)
data_path = resource_filename("pypredict","data/")
sat_u = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s1 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2.setTLE("1 44814U 19081L 20321.73053029 .00001305 00000-0 63025-4 0 9996",
"2 44814 97.4788 21.6285 0013387 80.2501 280.0246 15.20374749 54001")
with open("u_TLEs.txt", 'r') as u_TLEs:
sat_u_TLEs = u_TLEs.readlines()
with open("s1_TLEs.txt", 'r') as s1_TLEs:
sat_s1_TLEs = s1_TLEs.readlines()
date0 = datetime(2020, 11, 17, 00, 13, 33, 0)
dep_date = date0 + timedelta(minutes=10)
initial_date = dep_date + timedelta(days=3)
ekf = EKF()
meas_dt = 2.0#0.2
x = zeros([6,1])
iterations = 1
start = datetime.utcnow()
minutes = zeros(42)
for i in range(21):
minutes[2*i] = i*5.0 - meas_dt/60
minutes[2*i + 1] = i*5.0
len_minutes = len(minutes)
e_rcrb = zeros(len_minutes)
bias = zeros(len_minutes)
acum_est = zeros([len_minutes, 3, 1])
real_u = zeros([len_minutes, 3, 1])
real_s1 = zeros([len_minutes, 3, 1])
real_s2 = zeros([len_minutes, 3, 1])
rmse = zeros(len_minutes)
for l0 in L0:
print("Deployment: {}".format(l0))
sat_u.setTLE(sat_u_TLEs[1 + l0*3], sat_u_TLEs[2 + l0*3])
sat_s1.setTLE(sat_s1_TLEs[1 + l0*3], sat_s1_TLEs[2 + l0*3])
acum_est = acum_est*0
for l in range(L):
studied_date = initial_date
studied_date -= timedelta(seconds=2*meas_dt)
d = 0
e, GNSS_noise = self.generate_noise(std_RD, std_AOA, std_GNSS, len_minutes)
for t in range(21):
studied_date += timedelta(seconds=meas_dt)
if (l==0):
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
real_u[2*t] = sat_u.getXYZ().copy()
real_s1[2*t] = sat_s1.getXYZ().copy()
real_s2[2*t] = sat_s2.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(real_s1[2*t], real_s2[2*t], GNSS_noise[:,d])
k_w_GNSS_error = self.loc.get_real_vector(real_u[2*t], noisy_s1, noisy_s2)
u_hat1 = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e[:,d], Q)
acum_est[2*t] += u_hat1
MSE = self.loc.get_MSE(u_hat1, noisy_s1, noisy_s2, k_w_GNSS_error, Q)
e_rcrb[2*t] += MSE[0,0] + MSE[1,1] + MSE[2,2]
rmse[2*t] += (u_hat1[0,0] - real_u[2*t,0,0])**2 + (u_hat1[1,0] - real_u[2*t,1,0])**2 + (u_hat1[2,0] - real_u[2*t,2,0])**2
d += 1
studied_date += timedelta(seconds=meas_dt)
sat_s1.updateOrbitalParameters(studied_date)
if (l==0):
sat_u.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
real_u[2*t+1] = sat_u.getXYZ().copy()
real_s1[2*t+1] = sat_s1.getXYZ().copy()
real_s2[2*t+1] = sat_s2.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(real_s1[2*t+1], real_s2[2*t+1], GNSS_noise[:,d])
k_w_GNSS_error = self.loc.get_real_vector(real_u[2*t+1], noisy_s1, noisy_s2)
u_hat2 = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e[:,d], Q)
acum_est[2*t+1] += u_hat2
MSE = self.loc.get_MSE(u_hat2, noisy_s1, noisy_s2, k_w_GNSS_error, Q)
e_rcrb[2*t+1] += MSE[0,0] + MSE[1,1] + MSE[2,2]
rmse[2*t+1] += (u_hat2[0,0] - real_u[2*t+1,0,0])**2 + (u_hat2[1,0] - real_u[2*t+1,1,0])**2 + (u_hat2[2,0] - real_u[2*t+1,2,0])**2
d += 1
studied_date += timedelta(seconds=300-2*meas_dt)
acum_est = acum_est/L
for j, u in enumerate(real_u):
bias[j] += (acum_est[j,0,0] - u[0,0])**2 + (acum_est[j,1,0] - u[1,0])**2 + (acum_est[j,2,0] - u[2,0])**2
print("Time3: {}".format((studied_date - initial_date).total_seconds()))
e_rcrb = sqrt(e_rcrb/len(L0)/L)
rmse = sqrt(rmse/len(L0)/L)
bias = sqrt(bias/len(L0))
with open(output, 'w') as data_file_rcrb:
outRCRB = csv.writer(data_file_rcrb)
outRCRB.writerow(["RMSE", "e RCRB", "Bias"])
for i in range(len(e_rcrb)):
outRCRB.writerow([rmse[i], e_rcrb[i], bias[i]])
for i in range(len(e_rcrb), len(rmse)):
outRCRB.writerow([rmse[i]])
finish = datetime.utcnow()
print("Start: {}\nFinish: {}\nDelta: {} minutes".format(start, finish, (finish-start).total_seconds()/60))
def plot_data(self, rcrb_file="worst_rcrb.csv", rmse_file="rmse.csv", model=1):
div = 32
finer_minutes = arange(0, div*100+1, dtype="float")/div
len_finer_minutes = len(finer_minutes)
rcrb = zeros(len_finer_minutes)
dist_u_s1 = zeros(len_finer_minutes)
dist_u_s2 = zeros(len_finer_minutes)
alpha_max = zeros(len_finer_minutes)
alpha_mean = zeros(len_finer_minutes)
alpha_min = zeros(len_finer_minutes)
meas_dt = 2.0#0.13#0.2
model_dt = 0.01
model_rmse = zeros(2*int(300/model_dt) - 2)
if (model):
minutes = zeros(26)
for i in range(13):
minutes[2*i] = (i*300.0 - meas_dt + 600.0)/60.0
minutes[2*i + 1] = (i*300.0 + 600.0)/60.0
ind0 = 320
indf = 2241
else:
minutes = zeros(42)
for i in range(21):
minutes[2*i] = i*5.0 - meas_dt/60
minutes[2*i + 1] = i*5.0
ind0 = 0
indf = len_finer_minutes
len_minutes = len(minutes)
e_rcrb = zeros(len_minutes)
bias = zeros(len_minutes)
rmse = zeros(len_minutes)
with open(rcrb_file, newline='') as f:
reader = csv.reader(f, delimiter=",")
next(reader)
data = array(list(reader), dtype=object)
for i in range(len(data)):
rcrb[i] = float(data[i,0])
dist_u_s1[i] = float(data[i,1])
dist_u_s2[i] = float(data[i,2])
alpha_min[i] = float(data[i,3])
alpha_mean[i] = float(data[i,4])
alpha_max[i] = float(data[i,5])
with open(rmse_file, newline='') as f:
reader = csv.reader(f, delimiter=",")
next(reader)
data = array(list(reader), dtype=object)
if (model):
for i in range(len(model_rmse)):
model_rmse[i] = float(data[i][0])
for i in range(len(e_rcrb)):
e_rcrb[i] = float(data[i][1])
bias[i] = float(data[i][2])
rmse[i] = float(data[i][3])
else:
for i in range(len(e_rcrb)):
rmse[i] = float(data[i][0])
e_rcrb[i] = float(data[i][1])
bias[i] = float(data[i][2])
rmse_time = []
time = 2100
for j in range(2*int(300/model_dt) - 2):
time += model_dt
rmse_time.append(time/60)
fig, ax = subplots(1, 1)
if (model):
ax.set_xlim(10, 70)
ax.set_ylim(10, 1000000)
else:
ax.set_xlim(0,100)
ax.set_ylim(1, 10000)
ax2 = ax.twinx()
ax2.set_ylim(0, 180)
ax.set_zorder(10)
ax.patch.set_visible(False)
#ax2.plot(finer_minutes[ind0:indf], alpha_max[ind0:indf], '-', linewidth=2.0, markersize=12,
# label="{} (max)".format(r'$\alpha$'), color="k")
ax2.plot(finer_minutes[ind0:indf], alpha_mean[ind0:indf], '-', linewidth=2.0, markersize=12,
label="{} (mean)".format(r'$\alpha$'), color="dimgrey")
#ax2.plot(finer_minutes[ind0:indf], alpha_min[ind0:indf], '-', linewidth=2.0, markersize=12,
# label="{} (min)".format(r'$\alpha$'), color="lightgrey")
ax.semilogy(finer_minutes[ind0:indf], rcrb[ind0:indf], '-', linewidth=2.0, markersize=12,
label="Root CRB", color="tab:orange")#, alpha=0.7)
ax.semilogy(finer_minutes[ind0:indf], dist_u_s1[ind0:indf], '-', linewidth=2.0, markersize=12,
label=r'$||\mathbf{u} - \mathbf{s_1}||$', color="tab:purple", alpha=0.7)
#ax.semilogy(finer_minutes, dist_u_s2, '-', linewidth=2.0, markersize=12,
# label=r'$||\mathbf{u} - \mathbf{s_2}||$', color="tab:red")
#ax.semilogy(minutes, e_rcrb, '-', linewidth=2.0, markersize=12,
# label="Root CRB", color="tab:red", alpha=0.7)
ax.semilogy(minutes, rmse, 'o', linewidth=2.0, markersize=12, fillstyle="none",
label="Measurement RMSE", color="tab:blue", alpha=0.7)
if (model):
ax.semilogy(rmse_time, model_rmse, '-', linewidth=2.0, markersize=12,
label="Model RMSE", color="tab:blue", alpha=0.7)
ax.semilogy(minutes, bias, '+', linewidth=2.0, markersize=12,# clip_on=False,
label="Bias", color="tab:green")
ax.grid()
ax.set(xlabel="Time [min]",
ylabel="RCRB, RMSE, bias and {} [m]".format(r'$||\mathbf{u} - \mathbf{s_1}||$'))
ax2.set_ylabel("{} [deg]".format(r'$\alpha$'))
ax.xaxis.label.set_size(16)
ax.yaxis.label.set_size(16)
ax2.yaxis.label.set_size(16)
ax.tick_params(which="both", direction="in", labelsize=14,
bottom=True, top=True, left=True, right=False)
ax2.tick_params(which="both", direction="in", labelsize=14,
bottom=False, top=False, left=False, right=True)
if (model):
ax.set_xticks([10, 20, 30, 40, 50, 60, 70])
else:
ax.set_xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
tight_layout()
show()
def plot_estimated_distance(self, l0=425, L=1, output="model.csv"):
start = datetime.utcnow()
std_RD = 10.0
std_AOA = 1.0*pi/180.0
std_GNSS = 10.0
Q = self.loc.get_Q(std_RD, std_AOA)
data_path = resource_filename("pypredict","data/")
sat_u = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s1 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2 = Sat(name="FLOCK 4P-1", tlepath="{}planet.txt".format(data_path), cat="Planet Labs")
sat_s2.setTLE("1 44814U 19081L 20321.73053029 .00001305 00000-0 63025-4 0 9996",
"2 44814 97.4788 21.6285 0013387 80.2501 280.0246 15.20374749 54001")
with open("u_TLEs.txt", 'r') as u_TLEs:
sat_u_TLEs = u_TLEs.readlines()
with open("s1_TLEs.txt", 'r') as s1_TLEs:
sat_s1_TLEs = s1_TLEs.readlines()
date0 = datetime(2020, 11, 17, 00, 13, 33, 0)
dep_date = date0 + timedelta(minutes=10)
initial_date = dep_date + timedelta(days=3)
time = arange(0, 6000)#*2
dist_u_s1 = zeros(len(time))
dist_u_s2 = zeros(len(time))
est_dist_u_s1 = zeros(len(time))
#est_dist_u_s2 = zeros(len(time))
dist_diff_between_meas = zeros(len(time)-1)
mean_est_dist_u_s1 = zeros(len(time))
#mean_est_dist_u_s2 = zeros(len(time))
meas_error = zeros(len(time))
mean_meas_error = zeros(len(time))
mean_meas_and_model_error = zeros(len(time))
u_hat = zeros([len(time), 3, 1])
all_u_hat = zeros([len(time), 3, 1])
all_u = zeros([len(time), 3, 1])
all_s1 = zeros([len(time), 3, 1])
all_s2 = zeros([len(time), 3, 1])
sat_u.setTLE(sat_u_TLEs[1 + l0*3], sat_u_TLEs[2 + l0*3])
sat_s1.setTLE(sat_s1_TLEs[1 + l0*3], sat_s1_TLEs[2 + l0*3])
for i in range(len(time)):
studied_date = initial_date + timedelta(seconds=float(time[i]))
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
all_u[i,:] = sat_u.getXYZ().copy()
all_s1[i,:] = sat_s1.getXYZ().copy()
all_s2[i,:] = sat_s2.getXYZ().copy()
dist_u_s1[i] = linalg.norm(all_u[i,:] - all_s1[i,:])#*0.001
dist_u_s2[i] = linalg.norm(all_u[i,:] - all_s2[i,:])#*0.001
for l in range(L):
current_time = datetime.utcnow()
print("\nSimulation {}/{}, Elapsed time: {:.2f} minutes".format(l+1, L,
(current_time - start).total_seconds()/60))
e, GNSS_noise = self.generate_noise(std_RD, std_AOA, std_GNSS, len(time))
peak1 = 0
peak2 = 0
peak3 = 0
for i in range(len(time)):
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(all_s1[i,:], all_s2[i,:], GNSS_noise[:,i])
k_w_GNSS_error = self.loc.get_real_vector(all_u[i,:], noisy_s1, noisy_s2)
u_hat[i,:] = self.loc.estimate(noisy_s1, noisy_s2, k_w_GNSS_error, e[:,i], Q)
est_dist_u_s1[i] = linalg.norm(u_hat[i] - noisy_s1)
#est_dist_u_s2[i] = linalg.norm(u_hat[i] - noisy_s2)
meas_error[i] = linalg.norm(u_hat[i,:] - all_u[i,:])
dist_diff_between_meas = abs(est_dist_u_s1[1:] - est_dist_u_s1[:-1])
peak1 = dist_diff_between_meas.argmax()
if (dist_diff_between_meas[:peak1-840].size > 0):
peak2 = dist_diff_between_meas[:peak1-840].argmax()
if (dist_diff_between_meas[peak1+600:].size > 0):
peak3 = dist_diff_between_meas[peak1+600:].argmax() + peak1 + 600
if (dist_diff_between_meas[peak3] > dist_diff_between_meas[peak2]):
peak2 = peak3
if (peak2 < peak1):
aux = peak1
peak1 = peak2
peak2 = aux
print("Peak 1: {:.4f}, Peak 2 : {:.4f}, Peak3: {:.4f}".format(peak1/60, peak2/60, peak3/60))
print("Peak 1: {:.2f}, Peak 2 : {:.2f}, Peak3: {:.2f}".format(dist_diff_between_meas[peak1], dist_diff_between_meas[peak2], dist_diff_between_meas[peak3]))
#print(meas_error.argmax()/60)
mean_est_dist_u_s1 += est_dist_u_s1
#mean_est_dist_u_s2 += est_dist_u_s2
mean_meas_error += meas_error
meas_and_model_error = self.model_sim(sat_u, sat_s1, sat_s2, u_hat, peak1-420, peak1+300, initial_date, GNSS_noise, e, meas_error)
meas_and_model_error = self.model_sim(sat_u, sat_s1, sat_s2, u_hat, peak2-420, peak2+300, initial_date, GNSS_noise, e, meas_and_model_error)
mean_meas_and_model_error += meas_and_model_error
all_u_hat += u_hat
mean_est_dist_u_s1[:] = mean_est_dist_u_s1/L
#mean_est_dist_u_s2[:] = mean_est_dist_u_s2/L
mean_meas_error[:] = mean_meas_error/L
mean_meas_and_model_error[:] = mean_meas_and_model_error/L
all_u_hat[:] = all_u_hat/L
with open(output, 'w') as data_file:
outRCRB = csv.writer(data_file)
outRCRB.writerow(["Time", "Mean measurement error", "Mean measurement and model error", "Mean estimated distance u-s1", "Distance u-s1", "All u_x", "All u_y", "All u_z", "All u_hat_x", "All u_hat_y", "All u_hat_z"])
for i in range(len(time)):
outRCRB.writerow([time[i], mean_meas_error[i], mean_meas_and_model_error[i],
mean_est_dist_u_s1[i], dist_u_s1[i],
all_u[i,0,0], all_u[i,1,0], all_u[i,2,0],
all_u_hat[i,0,0], all_u_hat[i,1,0], all_u_hat[i,2,0]])
end = datetime.utcnow()
print("Duration: {}".format(end-start))
self.plot_model(output)
def model_sim(self, sat_u, sat_s1, sat_s2, u_hat, peak_t0, peak_tf, initial_date, GNSS_noise, e, meas_error):
std_RD = 10.0
std_AOA = 1.0*pi/180.0
std_GNSS = 10.0
t0 = float(peak_t0)
studied_date = initial_date + timedelta(seconds=t0)
sat_s1.updateOrbitalParameters(studied_date)
sat_s2.updateOrbitalParameters(studied_date + timedelta(seconds=4))
s1 = sat_s1.getXYZ().copy()
s2 = sat_s2.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(s1, s2, GNSS_noise[:,int(t0)])
u_vel = (u_hat[peak_t0,:] - u_hat[peak_t0-1,:])/1
model_dt = 0.01
seconds = int(peak_tf - t0)
N = int(seconds/model_dt)
x = zeros([6,1])
iterations = 1
h = model_dt/iterations
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1)
self.mainSat_v = self.Q_poQ_ip*sat_s1.getInertialVel().copy()
lvlh_u = self.transformPosition(u_hat[peak_t0,:])
lvlh_v = self.transformVelocity(u_vel)
x[0:3] = lvlh_u.copy()
x[3:6] = lvlh_v.copy()
sat_s1_n = sat_s1.n
rc = sat_s1.a*sqrt(1 - sat_s1.e**2)
meas_and_model_error = meas_error.copy()
studied_date = initial_date + timedelta(seconds=t0)
one_div_model_dt = int(1/model_dt)
for s in range(seconds):
e, GNSS_noise = self.generate_noise(std_RD, std_AOA, std_GNSS, one_div_model_dt)
for i in range(one_div_model_dt):#N):
studied_date += timedelta(seconds=model_dt)
sat_u.updateOrbitalParameters(studied_date)
sat_s1.updateOrbitalParameters(studied_date)
u = sat_u.getXYZ().copy()
s1 = sat_s1.getXYZ().copy()
noisy_s1, noisy_s2 = self.loc.add_GNSS_error(s1, s2, GNSS_noise[:,i])
self.initTransformationData(sat_s1.incl, sat_s1.RAAN, sat_s1.theta, sat_s1.w, noisy_s1)
model_pos = self.ekf.RK4(x, h, iterations, sat_s1_n, rc)
x = model_pos.copy()
new_u_hat = self.orbital2inertial(x[:3])
meas_and_model_error[s+int(t0)+1] = linalg.norm(new_u_hat - u)
u_hat[s+int(t0)+1,:] = new_u_hat
return meas_and_model_error
def plot_model(self, filename="model4.csv"):
time = arange(0, 6000)#*2
dist_u_s1 = zeros(len(time))
dist_u_s2 = zeros(len(time))
est_dist_u_s1 = zeros(len(time))
#est_dist_u_s2 = zeros(len(time))
dist_diff_between_meas = zeros(len(time)-1)
mean_est_dist_u_s1 = zeros(len(time))
#mean_est_dist_u_s2 = zeros(len(time))
meas_error = zeros(len(time))
mean_meas_error = zeros(len(time))
mean_meas_and_model_error = zeros(len(time))
all_u_hat = zeros([len(time), 3, 1])
all_u = zeros([len(time), 3, 1])
all_s1 = zeros([len(time), 3, 1])
all_s2 = zeros([len(time), 3, 1])
with open(filename, newline='') as f: