''' '''
'''
ISC License
Copyright (c) 2016-2017, Autonomous Vehicle Systems Lab, University of Colorado at Boulder
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted, provided that the above
copyright notice and this permission notice appear in all copies.
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
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WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
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OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
'''
# This test validates the EKF module by running several
# scenarios on both individual functions and the full module.
# Author: Thibaud Teil
import numpy as np
import pytest
from Basilisk.architecture import messaging
from Basilisk.fswAlgorithms import sunlineSEKF
from Basilisk.utilities import SimulationBaseClass
from Basilisk.utilities import macros, RigidBodyKinematics
from Basilisk.utilities import unitTestSupport # general support file with common unit test functions
import SunLineSEKF_test_utilities as FilterPlots
def addTimeColumn(time, data):
return np.transpose(np.vstack([[time], np.transpose(data)]))
def setupFilterData(filterObject):
filterObject.sensorUseThresh = 0.
filterObject.state = [0.1, 0.9, 0.1, 0.0, 0.0]
filterObject.x = [1.0, 0.0, 1.0, 0.0, 0.1]
filterObject.covar = [0.4, 0.0, 0.0, 0.0, 0.0,
0.0, 0.4, 0.0, 0.0, 0.0,
0.0, 0.0, 0.4, 0.0, 0.0,
0.0, 0.0, 0.0, 0.004, 0.0,
0.0, 0.0, 0.0, 0.0, 0.004]
filterObject.qProcVal = 0.1**2
filterObject.qObsVal = 0.001
filterObject.eKFSwitch = (4./3)**2 #If low (0-5), the CKF kicks in easily, if high (>10) it's mostly only EKF
[docs]def test_all_functions_sekf(show_plots):
"""Module Unit Test"""
[testResults, testMessage] = sunline_individual_test()
assert testResults < 1, testMessage
[testResults, testMessage] = StatePropStatic()
assert testResults < 1, testMessage
[testResults, testMessage] = StatePropVariable(show_plots)
assert testResults < 1, testMessage
# uncomment this line is this test is to be skipped in the global unit test run, adjust message as needed
# @pytest.mark.skipif(conditionstring)
# uncomment this line if this test has an expected failure, adjust message as needed
# @pytest.mark.xfail(True)
# The following 'parametrize' function decorator provides the parameters and expected results for each
# of the multiple test runs for this test.
@pytest.mark.parametrize("SimHalfLength, AddMeasNoise , testVector1 , testVector2, stateGuess", [
(200, True ,[-0.7, 0.7, 0.0] ,[0.8, 0.9, 0.0], [0.7, 0.7, 0.0, 0.0, 0.0]),
(2000, True ,[-0.7, 0.7, 0.0] ,[0.8, 0.9, 0.0], [0.7, 0.7, 0.0, 0.0, 0.0]),
(200, False ,[-0.7, 0.7, 0.0] ,[0.8, 0.9, 0.0], [0.7, 0.7, 0.0, 0.0, 0.0]),
(200, False ,[0., 1., 0.] ,[1., 0., 0.], [0.3, 0.0, 0.6, 0.0, 0.0]),
(200, True ,[0.5, 0.5, 0.] ,[0., 1., 0.], [0.7, 0.7, 0.0, 0.0, 0.0])
])
# uncomment this line is this test is to be skipped in the global unit test run, adjust message as needed
# @pytest.mark.skipif(conditionstring)
# uncomment this line if this test has an expected failure, adjust message as needed
# @pytest.mark.xfail() # need to update how the RW states are defined
# provide a unique test method name, starting with test_
def test_all_sunline_sekf(show_plots, SimHalfLength, AddMeasNoise, testVector1, testVector2, stateGuess):
[testResults, testMessage] = StateUpdateSunLine(show_plots, SimHalfLength, AddMeasNoise, testVector1, testVector2, stateGuess)
assert testResults < 1, testMessage
def sunline_individual_test():
# The __tracebackhide__ setting influences pytest showing of tracebacks:
# the mrp_steering_tracking() function will not be shown unless the
# --fulltrace command line option is specified.
__tracebackhide__ = True
testFailCount = 0 # zero unit test result counter
testMessages = [] # create empty list to store test log messages
numStates = 5
numObs = 3
###################################################################################
## Testing dynamics matrix computation
###################################################################################
inputStates = [2,1,0.75,0.1,0.4]
inputOmega_SB_S = [0.,0.1, 0.4]
bVec = [1.,0.,0.]
dt =0.5
dcm_BS = [1., 0., 0.,
0., 1., 0.,
0., 0., 1.]
# Fill in the variables for the test
dcm = sunlineSEKF.new_doubleArray(3 * 3)
for j in range(9):
sunlineSEKF.doubleArray_setitem(dcm, j, dcm_BS[j])
sunlineSEKF.sunlineSEKFComputeDCM_BS(inputStates[:3], bVec, dcm)
dcmOut = []
for j in range(9):
dcmOut.append(sunlineSEKF.doubleArray_getitem(dcm, j))
DCM_BS = np.array(dcmOut).reshape([3,3])
omega_SB_B = np.dot(DCM_BS, np.array(inputOmega_SB_S))
dtilde = RigidBodyKinematics.v3Tilde(np.array(inputStates)[:3])
dBS = np.dot(dtilde, DCM_BS)
expDynMat = np.zeros([numStates,numStates])
expDynMat[0:3, 0:3] = np.array(RigidBodyKinematics.v3Tilde(omega_SB_B))
expDynMat[0:3, 3:numStates] = -dBS[:, 1:]
dynMat = sunlineSEKF.new_doubleArray(numStates*numStates)
for i in range(numStates*numStates):
sunlineSEKF.doubleArray_setitem(dynMat, i, 0.0)
sunlineSEKF.sunlineDynMatrix(inputStates, bVec, dt, dynMat)
DynOut = []
for i in range(numStates*numStates):
DynOut.append(sunlineSEKF.doubleArray_getitem(dynMat, i))
DynOut = np.array(DynOut).reshape(numStates, numStates)
errorNorm = np.linalg.norm(expDynMat - DynOut)
if(errorNorm > 1.0E-10):
print(errorNorm, "Dyn Matrix")
testFailCount += 1
testMessages.append("Dynamics Matrix generation Failure Dyn " + "\n")
###################################################################################
## STM and State Test
###################################################################################
inputStates = [2,1,0.75,0.1,0.4]
inputOmega = [0.,0.1, 0.4]
bVec_test = [1,0,0]
dt = 0.5
stateTransition = sunlineSEKF.new_doubleArray(numStates*numStates)
states = sunlineSEKF.new_doubleArray(numStates)
bVec = sunlineSEKF.new_doubleArray(3)
for k in range(3):
sunlineSEKF.doubleArray_setitem(bVec, k, bVec_test[k])
for i in range(numStates):
sunlineSEKF.doubleArray_setitem(states, i, inputStates[i])
for j in range(numStates):
if i==j:
sunlineSEKF.doubleArray_setitem(stateTransition, numStates*i+j, 1.0)
else:
sunlineSEKF.doubleArray_setitem(stateTransition, numStates*i+j, 0.0)
sunlineSEKF.sunlineStateSTMProp(expDynMat.flatten().tolist(), bVec_test, dt, states, stateTransition)
PropStateOut = []
PropSTMOut = []
for i in range(numStates):
PropStateOut.append(sunlineSEKF.doubleArray_getitem(states, i))
for i in range(numStates*numStates):
PropSTMOut.append(sunlineSEKF.doubleArray_getitem(stateTransition, i))
dcm_BS = [1., 0., 0.,
0., 1., 0.,
0., 0., 1.]
# Fill in the variables for the test
dcm = sunlineSEKF.new_doubleArray(3 * 3)
for j in range(9):
sunlineSEKF.doubleArray_setitem(dcm, j, dcm_BS[j])
sunlineSEKF.sunlineSEKFComputeDCM_BS(inputStates[:3], bVec_test, dcm)
dcmOut = []
for j in range(9):
dcmOut.append(sunlineSEKF.doubleArray_getitem(dcm, j))
DCM_BS = np.array(dcmOut).reshape([3,3])
STMout = np.array(PropSTMOut).reshape([numStates,numStates])
StatesOut = np.array(PropStateOut)
expectedSTM = dt*np.dot(expDynMat, np.eye(numStates)) + np.eye(numStates)
expectedStates = np.zeros(numStates)
## Equations when removing the unobservable states from d_dot
expectedStates[3:numStates] = np.array(inputOmega)[1:3]
expectedStates[0:3] = np.array(inputStates)[0:3]+dt*np.cross(np.dot(DCM_BS,np.array(inputOmega)), np.array(inputStates)[0:3])
errorNormSTM = np.linalg.norm(expectedSTM - STMout)
errorNormStates = np.linalg.norm(expectedStates - StatesOut)
if(errorNormSTM > 1.0E-10):
testFailCount += 1
testMessages.append("STM Propagation Failure Dyn " + "\n")
if(errorNormStates > 1.0E-10):
testFailCount += 1
testMessages.append("State Propagation Failure Dyn " + "\n")
###################################################################################
## Test the H and yMeas matrix generation as well as the observation count
###################################################################################
numCSS = 4
cssCos = [np.cos(np.deg2rad(10.)), np.cos(np.deg2rad(25.)), np.cos(np.deg2rad(5.)), np.cos(np.deg2rad(90.))]
sensorTresh = np.cos(np.deg2rad(50.))
cssNormals = [1.,0.,0.,0.,1.,0., 0.,0.,1., 1./np.sqrt(2), 1./np.sqrt(2),0.]
dcmArray_BS = RigidBodyKinematics.MRP2C([0.1,-0.15,0.2])
dcm_BS = (dcmArray_BS.flatten()).tolist()
measMat = sunlineSEKF.new_doubleArray(8*numStates)
obs = sunlineSEKF.new_doubleArray(8)
yMeas = sunlineSEKF.new_doubleArray(8)
numObs = sunlineSEKF.new_intArray(1)
for i in range(8*numStates):
sunlineSEKF.doubleArray_setitem(measMat, i, 0.)
for i in range(8):
sunlineSEKF.doubleArray_setitem(obs, i, 0.0)
sunlineSEKF.doubleArray_setitem(yMeas, i, 0.0)
sunlineSEKF.sunlineHMatrixYMeas(inputStates, numCSS, cssCos, sensorTresh, cssNormals, obs, yMeas, numObs, measMat)
obsOut = []
yMeasOut = []
numObsOut = []
HOut = []
for i in range(8*numStates):
HOut.append(sunlineSEKF.doubleArray_getitem(measMat, i))
for i in range(8):
yMeasOut.append(sunlineSEKF.doubleArray_getitem(yMeas, i))
obsOut.append(sunlineSEKF.doubleArray_getitem(obs, i))
numObsOut.append(sunlineSEKF.intArray_getitem(numObs, 0))
#Fill in expected values for test
expectedH = np.zeros([8,numStates])
expectedY = np.zeros(8)
for j in range(3):
expectedH[j,0:3] = np.eye(3)[j,:]
expectedY[j] =np.array(cssCos[j]) - np.dot( np.array(inputStates)[0:3], np.array(cssNormals)[j*3:(j+1)*3])
expectedObs = np.array([np.cos(np.deg2rad(10.)), np.cos(np.deg2rad(25.)), np.cos(np.deg2rad(5.)),0.,0.,0.,0.,0.])
expectedNumObs = 3
HOut = np.array(HOut).reshape([8, numStates])
errorNorm = np.zeros(4)
errorNorm[0] = np.linalg.norm(HOut - expectedH)
errorNorm[1] = np.linalg.norm(yMeasOut - expectedY)
errorNorm[2] = np.linalg.norm(obsOut - expectedObs)
errorNorm[3] = np.linalg.norm(numObsOut[0] - expectedNumObs)
for i in range(4):
if(errorNorm[i] > 1.0E-10):
testFailCount += 1
testMessages.append("H and yMeas update failure \n")
###################################################################################
## Test the Kalman Gain
###################################################################################
numObs = 3
h = [1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
covar = [1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.,
0., 0., 1., 0., 0.,
1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.]
noise= 0.01
Kalman = sunlineSEKF.new_doubleArray(numStates * 8)
for i in range(8 * numStates):
sunlineSEKF.doubleArray_setitem(Kalman, i, 0.)
sunlineSEKF.sunlineKalmanGain(covar, h, noise, numObs, Kalman)
KalmanOut = []
for i in range(8 * numStates):
KalmanOut.append(sunlineSEKF.doubleArray_getitem(Kalman, i))
# Fill in expected values for test
Hmat = np.array(h).reshape([8,numStates])
Pk = np.array(covar).reshape([numStates,numStates])
R = noise*np.eye(numObs)
expectedK = np.dot(np.dot(Pk, Hmat[0:numObs,:].T), np.linalg.inv(np.dot(np.dot(Hmat[0:numObs,:], Pk), Hmat[0:numObs,:].T) + R[0:numObs,0:numObs]))
KalmanOut = np.array(KalmanOut)[0:numStates*numObs].reshape([numStates, numObs])
errorNorm = np.linalg.norm(KalmanOut[:,0:numObs] - expectedK)
if (errorNorm > 1.0E-10):
print(errorNorm, "Kalman Gain Error")
testFailCount += 1
testMessages.append("Kalman Gain update failure \n")
###################################################################################
## Test the EKF update
###################################################################################
KGain = [1., 2., 3., 0., 1., 1., 0., 1., 0., 1., 3., 0., 1., 0., 2.]
for i in range(numStates*8-numStates*numObs):
KGain.append(0.)
inputStates = [2,1,0.75,0.1,0.4]
xbar = [0.1, 0.2, 0.01, 0.005, 0.009]
numObs = 3
h = [1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
covar = [1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.,
0., 0., 1., 0., 0.,
1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.]
noise = 0.01
inputY = np.zeros(3)
for j in range(3):
inputY[j] = np.array(cssCos[j]) - np.dot(np.array(inputStates)[0:3], np.array(cssNormals)[j * 3:(j + 1) * 3])
inputY = inputY.tolist()
stateError = sunlineSEKF.new_doubleArray(numStates)
covarMat = sunlineSEKF.new_doubleArray(numStates*numStates)
inputs = sunlineSEKF.new_doubleArray(numStates)
for i in range(numStates):
sunlineSEKF.doubleArray_setitem(stateError, i, 0.)
sunlineSEKF.doubleArray_setitem(inputs, i, inputStates[i])
for j in range(numStates):
sunlineSEKF.doubleArray_setitem(covarMat,i+j,0.)
sunlineSEKF.sunlineSEKFUpdate(KGain, covar, noise, numObs, inputY, h, inputs, stateError, covarMat)
stateOut = []
covarOut = []
errorOut = []
for i in range(numStates):
stateOut.append(sunlineSEKF.doubleArray_getitem(inputs, i))
errorOut.append(sunlineSEKF.doubleArray_getitem(stateError, i))
for j in range(numStates*numStates):
covarOut.append(sunlineSEKF.doubleArray_getitem(covarMat, j))
# Fill in expected values for test
KK = np.array(KGain)[0:numStates*3].reshape([numStates,3])
expectedStates = np.array(inputStates) + np.dot(KK, np.array(inputY))
H = np.array(h).reshape([8,numStates])[0:3,:]
Pk = np.array(covar).reshape([numStates, numStates])
R = noise * np.eye(3)
expectedP = np.dot(np.dot(np.eye(numStates) - np.dot(KK, H), Pk), np.transpose(np.eye(numStates) - np.dot(KK, H))) + np.dot(KK, np.dot(R,KK.T))
errorNorm = np.zeros(2)
errorNorm[0] = np.linalg.norm(np.array(stateOut) - expectedStates)
errorNorm[1] = np.linalg.norm(expectedP - np.array(covarOut).reshape([numStates,numStates]))
for i in range(2):
if(errorNorm[i] > 1.0E-10):
testFailCount += 1
testMessages.append("EKF update failure \n")
###################################################################################
## Test the CKF update
###################################################################################
KGain = [1., 2., 3., 0., 1., 1., 0., 1., 0., 1., 3., 0., 1., 0., 2.]
for i in range(numStates * 8 - numStates * 3):
KGain.append(0.)
inputStates = [2,1,0.75,0.1,0.4]
xbar = [0.1, 0.2, 0.01, 0.005, 0.009]
h = [1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]
covar = [1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.,
0., 0., 1., 0., 0.,
1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.]
noise =0.01
inputY = np.zeros(numObs)
for j in range(numObs):
inputY[j] = np.array(cssCos[j]) - np.dot(np.array(inputStates)[0:3],
np.array(cssNormals)[j * 3:(j + 1) * 3])
inputY = inputY.tolist()
stateError = sunlineSEKF.new_doubleArray(numStates)
covarMat = sunlineSEKF.new_doubleArray(numStates * numStates)
for i in range(numStates):
sunlineSEKF.doubleArray_setitem(stateError, i, xbar[i])
for j in range(numStates):
sunlineSEKF.doubleArray_setitem(covarMat, i + j, 0.)
sunlineSEKF.sunlineCKFUpdate(xbar, KGain, covar, noise, numObs, inputY, h, stateError, covarMat)
covarOut = []
errorOut = []
for i in range(numStates):
errorOut.append(sunlineSEKF.doubleArray_getitem(stateError, i))
for j in range(numStates*numStates):
covarOut.append(sunlineSEKF.doubleArray_getitem(covarMat, j))
# Fill in expected values for test
KK = np.array(KGain)[0:numStates * numObs].reshape([numStates, numObs])
H = np.array(h).reshape([8, numStates])[0:3, :]
expectedStateError = np.array(xbar) + np.dot(KK, (np.array(inputY) - np.dot(H, np.array(xbar))))
Pk = np.array(covar).reshape([numStates, numStates])
expectedP = np.dot(np.dot(np.eye(numStates) - np.dot(KK, H), Pk), np.transpose(np.eye(numStates) - np.dot(KK, H))) + np.dot(KK,
np.dot(
R,
KK.T))
errorNorm = np.zeros(2)
errorNorm[0] = np.linalg.norm(np.array(errorOut) - expectedStateError)
errorNorm[1] = np.linalg.norm(expectedP - np.array(covarOut).reshape([numStates, numStates]))
for i in range(2):
if (errorNorm[i] > 1.0E-10):
testFailCount += 1
testMessages.append("CKF update failure \n")
###################################################################################
## Test the sunlineSEKFComputeDCM_BS method
###################################################################################
inputStates = [2, 1, 0.75, 0.1, 0.4]
sunheading = inputStates[:3]
bvec1 = [0., 1., 0.]
b1 = np.array(bvec1)
dcm_BS = [1., 0., 0.,
0., 1., 0.,
0., 0., 1.]
# Fill in expected values for test
DCM_exp = np.zeros([3,3])
W_exp = np.eye(numStates)
DCM_exp[:, 0] = np.array(inputStates[0:3]) / (np.linalg.norm(np.array(inputStates[0:3])))
DCM_exp[:, 1] = np.cross(DCM_exp[:, 0], b1) / np.linalg.norm(np.array(np.cross(DCM_exp[:, 0], b1)))
DCM_exp[:, 2] = np.cross(DCM_exp[:, 0], DCM_exp[:, 1]) / np.linalg.norm(
np.cross(DCM_exp[:, 0], DCM_exp[:, 1]))
# Fill in the variables for the test
dcm = sunlineSEKF.new_doubleArray(3 * 3)
for j in range(9):
sunlineSEKF.doubleArray_setitem(dcm, j, dcm_BS[j])
sunlineSEKF.sunlineSEKFComputeDCM_BS(sunheading, bvec1, dcm)
switchBSout = []
dcmOut = []
for j in range(9):
dcmOut.append(sunlineSEKF.doubleArray_getitem(dcm, j))
errorNorm = np.zeros(1)
errorNorm[0] = np.linalg.norm(DCM_exp - np.array(dcmOut).reshape([3, 3]))
for i in range(len(errorNorm)):
if (errorNorm[i] > 1.0E-10):
testFailCount += 1
testMessages.append("Frame switch failure \n")
###################################################################################
## Test the Switching method
###################################################################################
inputStates = [2,1,0.75,0.1,0.4]
bvec1 = [0.,1.,0.]
b1 = np.array(bvec1)
covar = [1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.,
0., 0., 1., 0., 0.,
1., 0., 0., 1., 0.,
0., 1., 0., 0., 1.]
noise =0.01
# Fill in expected values for test
DCM_BSold = np.zeros([3,3])
DCM_BSnew = np.zeros([3,3])
Switch = np.eye(numStates)
SwitchBSold = np.eye(numStates)
SwitchBSnew = np.eye(numStates)
DCM_BSold[:,0] = np.array(inputStates[0:3])/(np.linalg.norm(np.array(inputStates[0:3])))
DCM_BSold[:,1] = np.cross(DCM_BSold[:,0], b1)/np.linalg.norm(np.array(np.cross(DCM_BSold[:,0], b1)))
DCM_BSold[:,2] = np.cross(DCM_BSold[:,0], DCM_BSold[:,1])/np.linalg.norm(np.cross(DCM_BSold[:,0], DCM_BSold[:,1]))
SwitchBSold[3:5, 3:5] = DCM_BSold[1:3, 1:3]
b2 = np.array([1.,0.,0.])
DCM_BSnew[:,0] = np.array(inputStates[0:3])/(np.linalg.norm(np.array(inputStates[0:3])))
DCM_BSnew[:,1] = np.cross(DCM_BSnew[:,0], b2)/np.linalg.norm(np.array(np.cross(DCM_BSnew[:,0], b2)))
DCM_BSnew[:,2] = np.cross(DCM_BSnew[:,0], DCM_BSnew[:,1])/np.linalg.norm(np.cross(DCM_BSnew[:,0], DCM_BSnew[:,1]))
SwitchBSnew[3:5, 3:5] = DCM_BSnew[1:3, 1:3]
DCM_newOld = np.dot(DCM_BSnew.T, DCM_BSold)
Switch[3:5, 3:5] = DCM_newOld[1:3,1:3]
# Fill in the variables for the test
bvec = sunlineSEKF.new_doubleArray(3)
states = sunlineSEKF.new_doubleArray(numStates)
covarMat = sunlineSEKF.new_doubleArray(numStates * numStates)
# switchBS = sunlineSEKF.new_doubleArray(numStates * numStates)
for i in range(3):
sunlineSEKF.doubleArray_setitem(bvec, i, bvec1[i])
for i in range(numStates):
sunlineSEKF.doubleArray_setitem(states, i, inputStates[i])
for j in range(numStates*numStates):
sunlineSEKF.doubleArray_setitem(covarMat, j, covar[j])
# sunlineSEKF.doubleArray_setitem(switchBS, j, switchInput[j])
sunlineSEKF.sunlineSEKFSwitch(bvec, states, covarMat)
switchBSout = []
covarOut = []
stateOut = []
bvecOut = []
for i in range(3):
bvecOut.append(sunlineSEKF.doubleArray_getitem(bvec, i))
for i in range(numStates):
stateOut.append(sunlineSEKF.doubleArray_getitem(states, i))
for j in range(numStates*numStates):
covarOut.append(sunlineSEKF.doubleArray_getitem(covarMat, j))
expectedState = np.dot(Switch, np.array(inputStates))
Pk = np.array(covar).reshape([numStates, numStates])
expectedP = np.dot(Switch, np.dot(Pk, Switch.T))
errorNorm = np.zeros(3)
errorNorm[0] = np.linalg.norm(np.array(stateOut) - expectedState)
errorNorm[1] = np.linalg.norm(expectedP - np.array(covarOut).reshape([numStates, numStates]))
errorNorm[2] = np.linalg.norm(np.array(bvecOut) - b2)
# errorNorm[3] = np.linalg.norm(SwitchBSnew - np.array(switchBSout).reshape([numStates, numStates]))
for i in range(len(errorNorm)):
if (errorNorm[i] > 1.0E-10):
testFailCount += 1
testMessages.append("Frame switch failure \n")
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + " SEKF individual tests")
else:
print(str(testFailCount) + ' tests failed')
print(testMessages)
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
####################################################################################
# Test for the time and update with static states (zero d_dot)
####################################################################################
def StatePropStatic():
# The __tracebackhide__ setting influences pytest showing of tracebacks:
# the mrp_steering_tracking() function will not be shown unless the
# --fulltrace command line option is specified.
__tracebackhide__ = True
numStates = 5
numObs = 3
testFailCount = 0 # zero unit test result counter
testMessages = [] # create empty list to store test log messages
unitTaskName = "unitTask" # arbitrary name (don't change)
unitProcessName = "TestProcess" # arbitrary name (don't change)
# Create a sim module as an empty container
unitTestSim = SimulationBaseClass.SimBaseClass()
# Create test thread
testProcessRate = macros.sec2nano(0.5) # update process rate update time
testProc = unitTestSim.CreateNewProcess(unitProcessName)
testProc.addTask(unitTestSim.CreateNewTask(unitTaskName, testProcessRate))
# Construct algorithm and associated C++ container
module = sunlineSEKF.sunlineSEKF()
module.ModelTag = "sunlineSEKF"
# Add test module to runtime call list
unitTestSim.AddModelToTask(unitTaskName, module)
setupFilterData(module)
kfLog = module.logger(["covar", "state"], testProcessRate*10)
unitTestSim.AddModelToTask(unitTaskName, kfLog)
# connect messages
cssDataInMsg = messaging.CSSArraySensorMsg()
cssConfigInMsg = messaging.CSSConfigMsg()
module.cssDataInMsg.subscribeTo(cssDataInMsg)
module.cssConfigInMsg.subscribeTo(cssConfigInMsg)
unitTestSim.InitializeSimulation()
unitTestSim.ConfigureStopTime(macros.sec2nano(8000.0))
unitTestSim.ExecuteSimulation()
stateLog = unitTestSupport.addTimeColumn(kfLog.times(), kfLog.state)
for i in range(numStates):
if (abs(stateLog[-1, i + 1] - stateLog[0, i + 1]) > 1.0E-10):
testFailCount += 1
testMessages.append("State propagation failure \n")
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + "SEKF static state propagation")
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
####################################################################################
# Test for the time and update with changing states (non-zero d_dot)
####################################################################################
def StatePropVariable(show_plots):
# The __tracebackhide__ setting influences pytest showing of tracebacks:
# the mrp_steering_tracking() function will not be shown unless the
# --fulltrace command line option is specified.
__tracebackhide__ = True
testFailCount = 0 # zero unit test result counter
testMessages = [] # create empty list to store test log messages
unitTaskName = "unitTask" # arbitrary name (don't change)
unitProcessName = "TestProcess" # arbitrary name (don't change)
numStates = 5
# Create a sim module as an empty container
unitTestSim = SimulationBaseClass.SimBaseClass()
# Create test thread
testProcessRate = macros.sec2nano(0.5) # update process rate update time
testProc = unitTestSim.CreateNewProcess(unitProcessName)
testProc.addTask(unitTestSim.CreateNewTask(unitTaskName, testProcessRate))
# Construct algorithm and associated C++ container
module = sunlineSEKF.sunlineSEKF()
module.ModelTag = "sunlineSEKF"
# Add test module to runtime call list
unitTestSim.AddModelToTask(unitTaskName, module)
setupFilterData(module)
InitialState = (np.array(module.state)+ +np.array([0.,0.,0.,0.0001,0.002])).tolist()
Initialx = module.x
InitialCovar = module.covar
module.state = InitialState
kfLog = module.logger(["covar", "stateTransition", "state", "x"], testProcessRate)
unitTestSim.AddModelToTask(unitTaskName, kfLog)
# connect messages
cssDataInMsg = messaging.CSSArraySensorMsg()
cssConfigInMsg = messaging.CSSConfigMsg()
module.cssDataInMsg.subscribeTo(cssDataInMsg)
module.cssConfigInMsg.subscribeTo(cssConfigInMsg)
unitTestSim.InitializeSimulation()
unitTestSim.ConfigureStopTime(macros.sec2nano(1000.0))
unitTestSim.ExecuteSimulation()
covarLog = unitTestSupport.addTimeColumn(kfLog.times(), kfLog.covar)
stateLog = unitTestSupport.addTimeColumn(kfLog.times(), kfLog.state)
stateErrorLog = unitTestSupport.addTimeColumn(kfLog.times(), kfLog.x)
stmLog = unitTestSupport.addTimeColumn(kfLog.times(), kfLog.stateTransition)
bVec = [1.,0.,0.]
dt = 0.5
expectedStateArray = np.zeros([2001,numStates+1])
DCM_BS = np.zeros([2001,3,3])
omega_S = np.zeros([2001,3])
omega_B = np.zeros([2001,3])
expectedStateArray[0,1:numStates+1] = np.array(InitialState)
expDynMat = np.zeros([2001,numStates,numStates])
DCM_BS[0,:,0] = np.array(InitialState[0:3])/(np.linalg.norm(np.array(InitialState[0:3])))
DCM_BS[0,:,1] = np.cross(DCM_BS[0,:,0], bVec)/np.linalg.norm(np.array(np.cross(DCM_BS[0,:,0], bVec)))
DCM_BS[0,:,2] = np.cross(DCM_BS[0,:,0], DCM_BS[0,:,1])/np.linalg.norm(np.cross(DCM_BS[0,:,0], DCM_BS[0,:,1]))
omega_S[0,1:] = InitialState[3:]
omega_B[0,:] = np.dot(DCM_BS[0, :, :], omega_S[0,:])
for i in range(1,2001):
expectedStateArray[i,0] = dt*i*1E9
expectedStateArray[i,1:4] = expectedStateArray[i-1,1:4] + dt * np.cross(omega_B[i-1,:],
expectedStateArray[i - 1, 1:4])
expectedStateArray[i, 4:6] = expectedStateArray[i-1, 4:6]
# Fill in the variables for the test
dcm = sunlineSEKF.new_doubleArray(3 * 3)
for j in range(9):
sunlineSEKF.doubleArray_setitem(dcm, j, 0)
sunlineSEKF.sunlineSEKFComputeDCM_BS(expectedStateArray[i, 1:4], bVec, dcm)
dcmOut = []
for j in range(9):
dcmOut.append(sunlineSEKF.doubleArray_getitem(dcm, j))
DCM_BS[i,:,:] = np.array(dcmOut).reshape([3, 3])
omega_S[i, 1:] = expectedStateArray[i, 4:]
omega_B[i,:] = np.dot(DCM_BS[i, :, :], omega_S[i,:])
for i in range(0, 2001):
dtilde = -np.array(RigidBodyKinematics.v3Tilde(expectedStateArray[i, 1:4]))
dBS = np.dot(dtilde, DCM_BS[i,:,:])
expDynMat[i,0:3, 0:3] = np.array(RigidBodyKinematics.v3Tilde(omega_B[i,:]))
expDynMat[i, 0:3, 3:numStates] = dBS[:, 1:]
expectedSTM = np.zeros([2001,numStates,numStates])
expectedSTM[0,:,:] = np.eye(numStates)
for i in range(1,2001):
expectedSTM[i,:,:] = dt * np.dot(expDynMat[i-1,:,:], np.eye(numStates)) + np.eye(numStates)
expectedXBar = np.zeros([2001,numStates+1])
expectedXBar[0,1:6] = np.array(Initialx)
for i in range(1,2001):
expectedXBar[i,0] = dt*i*1E9
expectedXBar[i, 1:6] = np.dot(expectedSTM[i, :, :], expectedXBar[i - 1, 1:6])
expectedCovar = np.zeros([2001,26])
expectedCovar[0,1:26] = np.array(InitialCovar)
Gamma = np.zeros([2001,numStates, 2])
ProcNoiseCovar = np.zeros([2001,numStates,numStates])
for i in range(0,2001):
s_skew = np.array([[0., -expectedStateArray[i,3], expectedStateArray[i,2]],
[expectedStateArray[i,3], 0., -expectedStateArray[i,1]],
[-expectedStateArray[i,2], expectedStateArray[i,1], 0.]])
s_BS = np.dot(s_skew, DCM_BS[i,:,:])
Gamma[i, 0:3, 0:2] = dt ** 2. / 2. * s_BS[:,1:3]
Gamma[i,3:numStates, 0:2] = dt * np.eye(2)
ProcNoiseCovar[i,:,:] = np.dot(Gamma[i,:,:], np.dot(module.qProcVal*np.eye(2),Gamma[i,:,:].T))
for i in range(1,2001):
expectedCovar[i,0] = dt*i*1E9
expectedCovar[i,1:26] = (np.dot(expectedSTM[i,:,:], np.dot(np.reshape(expectedCovar[i-1,1:26],[numStates,numStates]), np.transpose(expectedSTM[i,:,:])))+ ProcNoiseCovar[i,:,:]).flatten()
FilterPlots.StatesVsExpected(stateLog, expectedStateArray, show_plots)
FilterPlots.StatesPlotCompare(stateErrorLog, expectedXBar, covarLog, expectedCovar, show_plots)
if (np.linalg.norm(np.array(stateLog)[:, 1:] - expectedStateArray[:, 1:]) > 1.0E-10):
testFailCount += 1
testMessages.append("General state propagation failure: State Prop \n")
if (np.linalg.norm(np.array(stateErrorLog)[:, 1:] - expectedXBar[:,1:]) > 1.0E-4):
testFailCount += 1
testMessages.append("General state propagation failure: State Error Prop \n")
if (np.linalg.norm(np.array(covarLog)[:, 1:] - expectedCovar[:, 1:]) > 1.0E-4):
testFailCount += 1
testMessages.append("General state propagation failure: Covariance Prop \n")
if (np.linalg.norm(np.array(stmLog)[:, 1:] - expectedSTM[:,:,:].reshape([2001,25])) > 1.0E-4):
testFailCount += 1
testMessages.append("General state propagation failure: STM Prop \n")
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + "SEKF general state propagation")
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
####################################################################################
# Test for the full filter with time and measurement update
####################################################################################
def StateUpdateSunLine(show_plots, SimHalfLength, AddMeasNoise, testVector1, testVector2, stateGuess):
# The __tracebackhide__ setting influences pytest showing of tracebacks:
# the mrp_steering_tracking() function will not be shown unless the
# --fulltrace command line option is specified.
__tracebackhide__ = True
testFailCount = 0 # zero unit test result counter
testMessages = [] # create empty list to store test log messages
numStates = 5
numObs = 3
unitTaskName = "unitTask" # arbitrary name (don't change)
unitProcessName = "TestProcess" # arbitrary name (don't change)
# Create a sim module as an empty container
unitTestSim = SimulationBaseClass.SimBaseClass()
# Create test thread
testProcessRate = macros.sec2nano(0.5) # update process rate update time
testProc = unitTestSim.CreateNewProcess(unitProcessName)
testProc.addTask(unitTestSim.CreateNewTask(unitTaskName, testProcessRate))
# Construct algorithm and associated C++ container
module = sunlineSEKF.sunlineSEKF()
module.ModelTag = "sunlineSEKF"
# Add test module to runtime call list
unitTestSim.AddModelToTask(unitTaskName, module)
setupFilterData(module)
# Set up some test parameters
cssConstelation = messaging.CSSConfigMsgPayload()
CSSOrientationList = [
[0.70710678118654746, -0.5, 0.5],
[0.70710678118654746, -0.5, -0.5],
[0.70710678118654746, 0.5, -0.5],
[0.70710678118654746, 0.5, 0.5],
[-0.70710678118654746, 0, 0.70710678118654757],
[-0.70710678118654746, 0.70710678118654757, 0.0],
[-0.70710678118654746, 0, -0.70710678118654757],
[-0.70710678118654746, -0.70710678118654757, 0.0],
]
CSSBias = [1 for i in range(len(CSSOrientationList))]
totalCSSList = []
# Initializing a 2D double array is hard with SWIG. That's why there is this
# layer between the above list and the actual C variables.
i = 0
for CSSHat in CSSOrientationList:
newCSS = messaging.CSSUnitConfigMsgPayload()
newCSS.CBias = CSSBias[i]
newCSS.nHat_B = CSSHat
totalCSSList.append(newCSS)
i = i + 1
cssConstelation.nCSS = len(CSSOrientationList)
cssConstelation.cssVals = totalCSSList
inputData = messaging.CSSArraySensorMsgPayload()
cssConstInMsg = messaging.CSSConfigMsg().write(cssConstelation)
cssDataInMsg = messaging.CSSArraySensorMsg()
# connect messages
module.cssDataInMsg.subscribeTo(cssDataInMsg)
module.cssConfigInMsg.subscribeTo(cssConstInMsg)
stateTarget1 = testVector1
stateTarget1 += [0.0, 0.0]
module.state = stateGuess
module.x = (np.array(stateTarget1) - np.array(stateGuess)).tolist()
kfLog = module.logger("x", testProcessRate)
dataLog = module.filtDataOutMsg.recorder()
unitTestSim.AddModelToTask(unitTaskName, dataLog)
unitTestSim.AddModelToTask(unitTaskName, kfLog)
unitTestSim.InitializeSimulation()
for i in range(SimHalfLength):
if i > 20:
dotList = []
for element in CSSOrientationList:
if AddMeasNoise:
dotProd = np.dot(np.array(element), np.array(testVector1)[0:3]) + np.random.normal(0., module.qObsVal)
else:
dotProd = np.dot(np.array(element), np.array(testVector1)[0:3])
dotList.append(dotProd)
inputData.CosValue = dotList
cssDataInMsg.write(inputData, unitTestSim.TotalSim.CurrentNanos)
unitTestSim.ConfigureStopTime(macros.sec2nano((i + 1) * 0.5))
unitTestSim.ExecuteSimulation()
stateLog = addTimeColumn(dataLog.times(), dataLog.state)
covarLog = addTimeColumn(dataLog.times(), dataLog.covar)
for i in range(numStates):
if (abs(covarLog[-1, i *numStates + 1 + i] - covarLog[0, i * numStates + 1 + i] / 100.) > 1E-1):
print(abs(covarLog[-1, i *numStates + 1 + i] - covarLog[0, i * numStates + 1 + i] / 100.))
testFailCount += 1
testMessages.append("Covariance update failure")
if (abs(stateLog[-1, i + 1] - stateTarget1[i]) > 1.0E-1):
testFailCount += 1
testMessages.append("State update failure")
stateTarget2 = testVector2
stateTarget2 = stateTarget2+[0.,0.]
inputData = messaging.CSSArraySensorMsgPayload()
for i in range(SimHalfLength):
if i > 20:
dotList = []
for element in CSSOrientationList:
if AddMeasNoise:
dotProd = np.dot(np.array(element), np.array(testVector2)[0:3]) + np.random.normal(0., module.qObsVal)
else:
dotProd = np.dot(np.array(element), np.array(testVector2)[0:3])
dotList.append(dotProd)
inputData.CosValue = dotList
cssDataInMsg.write(inputData, unitTestSim.TotalSim.CurrentNanos)
unitTestSim.ConfigureStopTime(macros.sec2nano((i + SimHalfLength+1) * 0.5))
unitTestSim.ExecuteSimulation()
stateErrorLog = unitTestSupport.addTimeColumn(kfLog.times(), kfLog.x)
stateLog = addTimeColumn(dataLog.times(), dataLog.state)
postFitLog = addTimeColumn(dataLog.times(), dataLog.postFitRes)
covarLog = addTimeColumn(dataLog.times(), dataLog.covar)
for i in range(numStates):
if (abs(covarLog[-1, i * numStates + 1 + i] - covarLog[0, i * numStates + 1 + i] / 100.) > 1E-1):
testFailCount += 1
testMessages.append("Covariance update failure at end")
if (abs(stateLog[-1, i + 1] - stateTarget2[i]) > 1.0E-1):
testFailCount += 1
testMessages.append("State update failure at end")
target1 = np.array(testVector1)
target2 = np.array(testVector2+[0.,0.])
FilterPlots.StatesPlot(stateErrorLog, covarLog, show_plots)
FilterPlots.StatesVsTargets(target1, target2, stateLog, show_plots)
FilterPlots.PostFitResiduals(postFitLog, module.qObsVal, show_plots)
# print out success message if no error were found
if testFailCount == 0:
print("PASSED: " + "SEKF full test")
else:
print(testMessages)
# return fail count and join into a single string all messages in the list
# testMessage
return [testFailCount, ''.join(testMessages)]
if __name__ == "__main__":
# StatePropVariable(True)
# sunline_individual_test()
test_all_sunline_sekf(True, 200, True ,[-0.7, 0.7, 0.0] ,[0.8, 0.9, 0.0], [0.7, 0.7, 0.0, 0.0, 0.0])