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/***********************************************/
/**
* @file covariancePod.cpp
*
* @brief Covariance matrix of kinematic orbits.
*
* @author Torsten Mayer-Guerr
* @date 2010-07-18
*
*/
/***********************************************/
#define DOCSTRING_CovariancePod
#include "base/import.h"
#include "config/configRegister.h"
#include "files/fileMatrix.h"
#include "files/fileInstrument.h"
#include "covariancePod.h"
/***********************************************/
GROOPS_REGISTER_CLASS_WITHOUT_SUBS(CovariancePod, "covariancePodType")
GROOPS_READCONFIG_CLASS(CovariancePod, "covariancePodType")
/***********************************************/
CovariancePod::CovariancePod(Config &config, const std::string &name)
{
try
{
FileName fileNameSigmaArc, fileNameSigmaEpoch, fileNameCovPodEpoch, fileNameCovFunc;
readConfigSequence(config, name, Config::MUSTSET, "", "");
readConfig(config, "sigma", sigma, Config::DEFAULT, "1", "general variance factor");
readConfig(config, "inputfileSigmasPerArc", fileNameSigmaArc, Config::OPTIONAL, "", "different accuaries for each arc (multplicated with sigma)");
readConfig(config, "inputfileSigmasPerEpoch", fileNameSigmaEpoch, Config::OPTIONAL, "", "different accuaries for each epoch (added)");
readConfig(config, "inputfileCovarianceFunction", fileNameCovFunc, Config::OPTIONAL, "", "covariances in time for along, cross, and radial direction");
readConfig(config, "inputfileCovariancePodEpoch", fileNameCovPodEpoch, Config::OPTIONAL, "", "3x3 epoch wise covariances");
endSequence(config);
if(isCreateSchema(config)) return;
if(!fileNameSigmaArc.empty())
readFileMatrix(fileNameSigmaArc, sigmaArc);
if(!fileNameSigmaEpoch.empty())
fileSigmaEpoch.open(fileNameSigmaEpoch);
if(!fileNameCovPodEpoch.empty())
fileCovPodEpoch.open(fileNameCovPodEpoch);
if(!fileNameCovFunc.empty())
readFileMatrix(fileNameCovFunc, covFunction);
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
void CovariancePod::testInput(const OrbitArc &pod, const ObservationSigmaArc &sigmaEpoch, const Covariance3dArc &covPod, const_MatrixSliceRef covFunction)
{
if(sigmaEpoch.size() && (sigmaEpoch.size() != pod.size()))
throw(Exception("sigma per epoch not compatible with this arc number"));
if((covPod.size() != 0) && (covPod.size() != pod.size()))
throw(Exception("orbit and CovariancePodEpoch are not compatible"));
if(covFunction.size() && (covFunction.rows()<pod.size()))
throw(Exception("covariance function to short for this arc"));
}
/***********************************************/
Matrix CovariancePod::covariance(UInt arcNo, const OrbitArc &pod)
{
try
{
ObservationSigmaArc sigmaEpoch = fileSigmaEpoch.readArc(arcNo);
Covariance3dArc covPod = fileCovPodEpoch.readArc(arcNo);
testInput(pod, sigmaEpoch, covPod, covFunction);
if(sigmaArc.size() && (arcNo>=sigmaArc.size()))
throw(Exception("sigmasPerArc contain not enough rows for this arc number"));
Double sigma2 = pow(this->sigma, 2);
if(sigmaArc.size())
sigma2 *= pow(this->sigmaArc(arcNo), 2);
Matrix C(3*pod.size(), Matrix::TRIANGULAR);
for(UInt i=0; i<sigmaEpoch.size(); i++)
for(UInt k=0; k<3; k++)
C(3*i+k,3*i+k) += pow(sigmaEpoch.at(i).sigma, 2);
// special case 1: diagonal matrix
// -------------------------------
if((covPod.size()==0) && (covFunction.size()==0))
{
for(UInt i=0; i<C.rows(); i++)
C(i,i) += sigma2;
return C;
}
// special case 2: epoch block diagonal matrix
// -------------------------------------------
if(covPod.size() && (covFunction.size()==0))
{
for(UInt i=0; i<pod.size(); i++)
{
Matrix C3x3 = covPod.at(i).covariance.matrix();
axpy(sigma2, C3x3, C.slice(3*i,3*i,3,3));
}
return C;
}
// covariance function in orbit system
// -----------------------------------
const Double sampling = covFunction(1,0)-covFunction(0,0);
for(UInt z=0; z<pod.size(); z++)
for(UInt s=z; s<pod.size(); s++)
{
UInt idx = static_cast<UInt>(round((pod.at(s).time-pod.at(z).time).seconds()/sampling));
C(3*z+0, 3*s+0) += sigma2 * covFunction(idx, 1+0);
C(3*z+1, 3*s+1) += sigma2 * covFunction(idx, 1+1);
C(3*z+2, 3*s+2) += sigma2 * covFunction(idx, 1+2);
}
fillSymmetric(C);
// rotate and decorrelate epoch wise residuals
// -------------------------------------------
for(UInt i=0; i<pod.size(); i++)
{
// orbit system: z: radial, x: along, y: cross
Vector3d x;
if(i==0)
x = pod.at(i+1).position - pod.at(i).position;
else
x = pod.at(i).position - pod.at(i-1).position;
Vector3d z = normalize(pod.at(i).position);
Vector3d y = normalize(crossProduct(z, x));
x = crossProduct(y, z);
Matrix D = Rotary3d(x,y).matrix().trans(); // Rot from CRF to SRF
// epoch wise decorrelation
if(covPod.size()!=0)
{
Matrix V = covPod.at(i).covariance.matrix(); // 3x3 Epoch covariance
V.setType(Matrix::SYMMETRIC, Matrix::UPPER);
Vector eigen = eigenValueDecomposition(V);
Matrix V1 = V;
V1.column(0) *= std::sqrt(eigen(0));
V1.column(1) *= std::sqrt(eigen(1));
V1.column(2) *= std::sqrt(eigen(2));
D = D*V1*V.trans(); // W^-T im LORF
}
copy(D.trans() * C.row(3*i,3), C.row(3*i,3));
copy(C.column(3*i,3) * D, C.column(3*i,3));
}
return C;
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
void CovariancePod::decorrelate(UInt arcNo, const OrbitArc &pod, const std::list<MatrixSlice> &A)
{
try
{
ObservationSigmaArc sigmaEpoch = fileSigmaEpoch.readArc(arcNo);
Covariance3dArc covPod = fileCovPodEpoch.readArc(arcNo);
testInput(pod, sigmaEpoch, covPod, covFunction);
if(sigmaArc.size() && (arcNo>=sigmaArc.size()))
throw(Exception("sigmasPerArc contain not enough rows for this arc number"));
Double weight = 1./this->sigma;
if(sigmaArc.size() != 0)
weight *= 1./this->sigmaArc(arcNo);
// special case 1: diagonal matrix
// -------------------------------
if((covPod.size()==0) && (sigmaEpoch.size()==0) && (covFunction.size()==0))
{
for(MatrixSliceRef WA : A)
if(WA.size()) WA *= weight;
return;
}
// special case 2: epoch block diagonal matrix
// -------------------------------------------
if(covPod.size() && (covFunction.size()==0))
{
for(UInt i=0; i<pod.size(); i++)
{
Matrix W = covPod.at(i).covariance.matrix();
W.setType(Matrix::SYMMETRIC);
if(sigmaEpoch.size())
{
W(0,0) += sigmaEpoch.at(i).sigma;
W(1,1) += sigmaEpoch.at(i).sigma;
W(2,2) += sigmaEpoch.at(i).sigma;
}
cholesky(W);
for(MatrixSliceRef WA : A)
if(WA.size())
triangularSolve(weight, W.trans(), WA.row(3*i,3));
}
return;
}
decorrelate(pod, 1/weight, sigmaEpoch, covPod, covFunction, A);
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
Matrix CovariancePod::decorrelate(const OrbitArc &pod, Double sigmaArc, const ObservationSigmaArc &sigmaEpoch,
const Covariance3dArc &covPod, const_MatrixSliceRef covFunction,
const std::list<MatrixSlice> &A)
{
try
{
testInput(pod, sigmaEpoch, covPod, covFunction);
// rotate and decorrelate epoch wise residuals
// -------------------------------------------
for(UInt i=0; i<pod.size(); i++)
{
// orbit system: z: radial, x: along, y: cross
Vector3d x;
if(i==0)
x = pod.at(i+1).position - pod.at(i).position;
else
x = pod.at(i).position - pod.at(i-1).position;
Vector3d z = normalize(pod.at(i).position);
Vector3d y = normalize(crossProduct(z, x));
x = crossProduct(y, z);
Matrix D = Rotary3d(x,y).matrix().trans(); // Rot from CRF to SRF
// epoch wise decorrelation
if(covPod.size()!=0)
{
Matrix V = covPod.at(i).covariance.matrix(); // 3x3 Epoch covariance
V.setType(Matrix::SYMMETRIC, Matrix::UPPER);
Vector eigen = eigenValueDecomposition(V);
Matrix V1 = V;
V1.column(0) *= 1./sqrt(eigen(0));
V1.column(1) *= 1./sqrt(eigen(1));
V1.column(2) *= 1./sqrt(eigen(2));
D = D*V1*V.trans(); // W^-T im LORF
}
for(MatrixSliceRef WA : A)
if(WA.size())
copy(D * WA.row(3*i,3), WA.row(3*i,3));
}
// covariance function in orbit system
// -----------------------------------
Matrix W(3*pod.size(), Matrix::SYMMETRIC, Matrix::UPPER);
if(covFunction.size())
{
const Double sampling = covFunction(1,0)-covFunction(0,0);
for(UInt z=0; z<pod.size(); z++)
for(UInt s=z; s<pod.size(); s++)
{
UInt idx = static_cast<UInt>(round((pod.at(s).time-pod.at(z).time).seconds()/sampling));
W(3*z+0, 3*s+0) = sigmaArc*sigmaArc * covFunction(idx, 1+0);
W(3*z+1, 3*s+1) = sigmaArc*sigmaArc * covFunction(idx, 1+1);
W(3*z+2, 3*s+2) = sigmaArc*sigmaArc * covFunction(idx, 1+2);
}
}
for(UInt i=0; i<sigmaEpoch.size(); i++)
for(UInt k=0; k<3; k++)
W(3*i+k,3*i+k) += pow(sigmaEpoch.at(i).sigma, 2);
cholesky(W);
for(MatrixSliceRef WA : A)
if(WA.size())
triangularSolve(1., W.trans(), WA);
return W;
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
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