Generates sigma points and weights according to Van der Merwe's 2004 dissertation[1] for the UnscentedKalmanFilter class.
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| MerweScaledSigmaPoints (double alpha=1e-3, double beta=2, int kappa=3 - States) |
| Constructs a generator for Van der Merwe scaled sigma points. More...
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int | NumSigmas () |
| Returns number of sigma points for each variable in the state x. More...
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Matrixd< States, 2 *States+1 > | SquareRootSigmaPoints (const Vectord< States > &x, const Matrixd< States, States > &S) |
| Computes the sigma points for an unscented Kalman filter given the mean (x) and square-root covariance(S) of the filter. More...
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const Vectord< 2 *States+1 > & | Wm () const |
| Returns the weight for each sigma point for the mean. More...
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double | Wm (int i) const |
| Returns an element of the weight for each sigma point for the mean. More...
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const Vectord< 2 *States+1 > & | Wc () const |
| Returns the weight for each sigma point for the covariance. More...
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double | Wc (int i) const |
| Returns an element of the weight for each sigma point for the covariance. More...
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template<int States>
class frc::MerweScaledSigmaPoints< States >
Generates sigma points and weights according to Van der Merwe's 2004 dissertation[1] for the UnscentedKalmanFilter class.
It parametrizes the sigma points using alpha, beta, kappa terms, and is the version seen in most publications. Unless you know better, this should be your default choice.
[1] R. Van der Merwe "Sigma-Point Kalman Filters for Probabilitic
Inference in Dynamic State-Space Models" (Doctoral dissertation)
- Template Parameters
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States | The dimensionality of the state. 2 * States + 1 weights will be generated. |