gauss_markov¶
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class GaussMarkov¶
- #include <gauss_markov.h>
This module is used to apply a second-order bounded Gauss-Markov random walk on top of an upper level process. The intent is that the caller will perform the set methods (setUpperBounds, setNoiseMatrix, setPropMatrix) as often as they need to, call computeNextState, and then call getCurrentState cyclically.
Public Functions
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GaussMarkov()¶
The constructor initialies the random number generator used for the walks
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GaussMarkov(uint64_t size, uint64_t newSeed = 0x1badcad1)¶
class constructor
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~GaussMarkov()¶
The destructor is a placeholder for one that might do something
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void computeNextState()¶
This method performs almost all of the work for the Gauss Markov random walk. It uses the current random walk configuration, propagates the current state, and then applies appropriate errors to the states to set the current error level.
- Returns
void
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inline void setRNGSeed(uint64_t newSeed)¶
Method does just what it says, seeds the random number generator.
- Parameters
newSeed – The seed to use in the random number generator
- Returns
void
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inline Eigen::VectorXd getCurrentState()¶
Method returns the current random walk state from model.
- Returns
The private currentState which is the vector of random walk values
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inline void setUpperBounds(Eigen::VectorXd newBounds)¶
Set the upper bounds on the random walk to newBounds.
- Parameters
newBounds – the bounds to put on the random walk states
- Returns
void
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inline void setNoiseMatrix(Eigen::MatrixXd noise)¶
Set the noiseMatrix that is used to define error sigmas.
- Parameters
noise – The new value to use for the noiseMatrix variable (error sigmas)
- Returns
void
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inline void setPropMatrix(Eigen::MatrixXd prop)¶
Set the propagation matrix that is used to propagate the state.
- Parameters
prop – The new value for the state propagation matrix
- Returns
void
Public Members
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Eigen::VectorXd stateBounds¶
— Upper bounds to use for markov
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Eigen::VectorXd currentState¶
— State of the markov model
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Eigen::MatrixXd propMatrix¶
— Matrix to propagate error state with
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Eigen::MatrixXd noiseMatrix¶
— Cholesky-decomposition or matrix square root of the covariance matrix to apply errors with
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BSKLogger bskLogger¶
— BSK Logging
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GaussMarkov()¶