gauss_markov
Variables
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static double DEFAULT_BOUND = -1.0
Default bound for the Gauss-Markov model.
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static double MIN_STATE_FACTOR = 1E-10
Minimum state factor for the Gauss-Markov model.
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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.
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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
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inline const Eigen::VectorXd &getCurrentState() const
Method returns the current random walk state from the model.
- Returns:
Reference to the vector of random walk values.
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inline void setUpperBounds(const Eigen::Ref<const Eigen::VectorXd> &newBounds)
Set the upper bounds on the random walk.
- Parameters:
newBounds – Bounds to put on the random walk states.
Public Members
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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
Private Functions
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void initializeRNG()
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GaussMarkov()