SRIleastSquares Class Reference

#include <SRIleastSquares.hpp>

Inheritance diagram for SRIleastSquares:

[legend]Collaboration diagram for SRIleastSquares:

[legend]List of all members.

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Detailed Description

class SRIleastSquares inherits SRI and implements a general least squares algorithm using SRI, including weighted, linear or linearized, robust and/or sequential algorithms.

At any point the state X and covariance P are related to the SRI by X = inverse(R) * z , P = inverse(R) * inverse(transpose(R)), or R = upper triangular square root (Cholesky decomposition) of the inverse of P, and z = R * X.

Definition at line 73 of file SRIleastSquares.hpp.

Public Member Functions
	SRIleastSquares (void) throw ()
	empty constructor
	SRIleastSquares (const unsigned int N) throw ()
	constructor given the dimension N.
	SRIleastSquares (const Namelist &NL) throw ()
	constructor given a Namelist; its dimension determines the SRI dimension.
	SRIleastSquares (const Matrix< double > &R, const Vector< double > &Z, const Namelist &NL) throw (MatrixException)
	explicit constructor - throw if the dimensions are inconsistent.
	SRIleastSquares (const SRIleastSquares &right) throw ()
	copy constructor
SRIleastSquares &	operator= (const SRIleastSquares &right) throw ()
	operator=
int	dataUpdate (Vector< double > &D, Vector< double > &X, Matrix< double > &Cov, void(LSF)(Vector< double > &X, Vector< double > &f, Matrix< double > &P)) throw (MatrixException)
	A general least squares update, NOT the SRIF (Kalman) measurement update.
void	zeroAll (void)
	remove all stored information by setting the SRI to zero (does not re-dimension).
bool	isValid ()
	Return true if the solution is valid, i.e. if the problem is non-singular.
void	Reset (const int N=0) throw (Exception)
	reset the computation, i.e.
Vector< double >	Solution (void)
	Get the current solution vector.
int	Iterations ()
	Get the number of iterations used in last call to leastSquaresEstimation().
double	Convergence ()
	Get the convergence value found in last call to leastSquaresEstimation().
double	ConditionNumber ()
	Get the condition number of the covariance matrix from last calls to leastSquaresEstimation() (Larger means 'closer to singular' except zero means condition number is infinite).
Public Attributes
int	iterationsLimit
	limit on the number of iterations
double	convergenceLimit
	limit on the RSS change in solution which produces success
double	divergenceLimit
	upper limit on the RSS change in solution which produces an abort
bool	doWeight
	if true, weight the equation using the inverse of covariance matrix on input - default is false
bool	doRobust
	if true, weight the equation using robust statistical techniques - default is false
bool	doSequential
	if true, save information for a sequential solution - default is false
bool	doLinearize
	if true, equation F(X)=D is non-linear, the algorithm will be iterated, and LSF must return partials matrix and F(X).
bool	doVerbose
	if true, output intermediate results
Friends
std::ostream &	operator<< (std::ostream &s, const SRIleastSquares &srif)
	output operator

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Constructor & Destructor Documentation

SRIleastSquares (  void   )  throw ()

empty constructor Definition at line 61 of file SRIleastSquares.cpp.

SRIleastSquares (  const unsigned int  N  )  throw ()

constructor given the dimension N. Parameters: N  dimension of the SRIleastSquares. Definition at line 66 of file SRIleastSquares.cpp.

SRIleastSquares (  const Namelist &  NL  )  throw ()

constructor given a Namelist; its dimension determines the SRI dimension. Parameters: NL  Namelist for the SRIleastSquares. Definition at line 77 of file SRIleastSquares.cpp.

SRIleastSquares (  const Matrix< double > &  R, const Vector< double > &  Z, const Namelist &  NL )  throw (MatrixException)

explicit constructor - throw if the dimensions are inconsistent. Parameters: R  Initial information matrix, an upper triangular matrix of dim N. Z  Initial information data vector, of length N. NL  Namelist for the SRIleastSquares, also of length N. Exceptions: MatrixException  if dimensions are not consistent. Definition at line 89 of file SRIleastSquares.cpp. References GPSTK_THROW.

SRIleastSquares (  const SRIleastSquares &  right  )  throw () [inline]

copy constructor Parameters: right  SRIleastSquares to be copied Definition at line 100 of file SRIleastSquares.hpp.

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Member Function Documentation

double ConditionNumber (   )  [inline]

Get the condition number of the covariance matrix from last calls to leastSquaresEstimation() (Larger means 'closer to singular' except zero means condition number is infinite). Definition at line 178 of file SRIleastSquares.hpp.

double Convergence (   )  [inline]

Get the convergence value found in last call to leastSquaresEstimation(). Returns: the convergence value Definition at line 173 of file SRIleastSquares.hpp.

int dataUpdate (  Vector< double > &  D, Vector< double > &  X, Matrix< double > &  Cov, void(LSF)(Vector< double > &X, Vector< double > &f, Matrix< double > &P)  )  throw (MatrixException)

A general least squares update, NOT the SRIF (Kalman) measurement update. Given data and measurement covariance, compute a solution and covariance using the appropriate least squares algorithm. Parameters: D  Data vector, length M Input: raw data Output: post-fit residuals X  Solution vector, length N Input: nominal solution X0 (zero when doLinearized is false) Output: final solution Cov  Covariance matrix, dimension (N,N) Input: (If doWeight is true) inverse measurement covariance or weight matrix(M,M) Output: Solution covariance matrix (N,N) LSF  Pointer to a function which is used to define the equation to be solved. LSF arguments are: X Nominal solution (input) f Values of the equation f(X) (length M) (output) P Partials matrix df/dX evaluated at X (dimension M,N) (output) When doLinearize is false, LSF ignores X and returns the (constant) partials matrix P and zero for f(X). Exceptions: MatrixException  if the input is inconsistent Return values: 0 ok -1 Problem is underdetermined (M<N) // TD -- naturalized sol? -2 Problem is singular -3 Algorithm failed to converge -4 Algorithm diverged Definition at line 271 of file SRIleastSquares.cpp. References gpstk::StringUtils::asString(), GPSTK_RETHROW, GPSTK_THROW, gpstk::inverse(), Cholesky::L, gpstk::Robust::MedianAbsoluteDeviation(), LabelledVector::message(), gpstk::RMS(), RobustTuningT, Vector::size(), and gpstk::SrifMU().

bool isValid (  void   )  [inline]

Return true if the solution is valid, i.e. if the problem is non-singular. Definition at line 154 of file SRIleastSquares.hpp.

int Iterations (   )  [inline]

Get the number of iterations used in last call to leastSquaresEstimation(). Returns: the number of iterations Definition at line 169 of file SRIleastSquares.hpp.

SRIleastSquares & operator= (  const SRIleastSquares &  right  )  throw ()

operator= Parameters: right  SRIleastSquares to be copied Definition at line 113 of file SRIleastSquares.cpp.

void Reset (  const int  N = 0  )  throw (Exception)

reset the computation, i.e. remove all stored information, and optionally change the dimension. If N is not input, the dimension is not changed. Parameters: N  new SRIleastSquares dimension (optional). Definition at line 542 of file SRIleastSquares.cpp. References GPSTK_RETHROW, and Vector::resize().

Vector<double> Solution (  void   )  [inline]

Get the current solution vector. Returns: current solution vector Definition at line 165 of file SRIleastSquares.hpp.

void zeroAll (  void   )

remove all stored information by setting the SRI to zero (does not re-dimension). Definition at line 530 of file SRIleastSquares.cpp.

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Friends And Related Function Documentation

std::ostream& operator<< (  std::ostream &  s, const SRIleastSquares &  srif )  [friend]

output operator

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Member Data Documentation

double convergenceLimit

limit on the RSS change in solution which produces success Definition at line 185 of file SRIleastSquares.hpp.

double divergenceLimit

upper limit on the RSS change in solution which produces an abort Definition at line 188 of file SRIleastSquares.hpp.

bool doLinearize

if true, equation F(X)=D is non-linear, the algorithm will be iterated, and LSF must return partials matrix and F(X). default is false Definition at line 203 of file SRIleastSquares.hpp.

bool doRobust

if true, weight the equation using robust statistical techniques - default is false Definition at line 196 of file SRIleastSquares.hpp.

bool doSequential

if true, save information for a sequential solution - default is false Definition at line 199 of file SRIleastSquares.hpp.

bool doVerbose

if true, output intermediate results Definition at line 206 of file SRIleastSquares.hpp.

bool doWeight

if true, weight the equation using the inverse of covariance matrix on input - default is false Definition at line 192 of file SRIleastSquares.hpp.

int iterationsLimit

limit on the number of iterations Definition at line 182 of file SRIleastSquares.hpp.

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The documentation for this class was generated from the following files:

• SRIleastSquares.hpp
• SRIleastSquares.cpp

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