Source code for test_SunLineSEKF

''' '''
'''
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 Copyright (c) 2016-2017, Autonomous Vehicle Systems Lab, University of Colorado at Boulder

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'''

#   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])