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| | EKF () |
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| | ~EKF () |
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| void | prediction (Eigen::Matrix< double, SIZE, 1 > f, Eigen::Matrix< double, SIZE, SIZE > J_F, Eigen::Matrix< double, SIZE, SIZE > Q) |
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| template<unsigned int MEASUREMENT_SIZE> |
Eigen::Matrix< double,
MEASUREMENT_SIZE, 1 > | innovation (Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > p, Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > h) |
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| template<unsigned int MEASUREMENT_SIZE> |
Eigen::Matrix< double,
MEASUREMENT_SIZE,
MEASUREMENT_SIZE > | innovationCovariance (Eigen::Matrix< double, MEASUREMENT_SIZE, SIZE > J_H, Eigen::Matrix< double, MEASUREMENT_SIZE, MEASUREMENT_SIZE > R) |
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| template<unsigned int MEASUREMENT_SIZE> |
| void | update (Eigen::Matrix< double, MEASUREMENT_SIZE, SIZE > J_H, Eigen::Matrix< double, SIZE, MEASUREMENT_SIZE > K, Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > y) |
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| template<unsigned int MEASUREMENT_SIZE> |
Eigen::Matrix< double, SIZE,
MEASUREMENT_SIZE > | gain (Eigen::Matrix< double, MEASUREMENT_SIZE, SIZE > J_H, Eigen::Matrix< double, MEASUREMENT_SIZE, MEASUREMENT_SIZE > S) |
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| template<unsigned int MEASUREMENT_SIZE> |
| void | correction (Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > p, Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > h, Eigen::Matrix< double, MEASUREMENT_SIZE, SIZE > J_H, Eigen::Matrix< double, MEASUREMENT_SIZE, MEASUREMENT_SIZE > R) |
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| template<unsigned int MEASUREMENT_SIZE, unsigned int DEGREE_OF_FREEDOM> |
| bool | correctionChiSquare (const Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > &p, const Eigen::Matrix< double, MEASUREMENT_SIZE, MEASUREMENT_SIZE > &R, const Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > &h, const Eigen::Matrix< double, MEASUREMENT_SIZE, SIZE > &J_H, float reject_threshold) |
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template<unsigned int SIZE>
template<unsigned int MEASUREMENT_SIZE>
| void ExtendedKalmanFilter::EKF< SIZE >::correction |
( |
Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > |
p, |
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Eigen::Matrix< double, MEASUREMENT_SIZE, 1 > |
h, |
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Eigen::Matrix< double, MEASUREMENT_SIZE, SIZE > |
J_H, |
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Eigen::Matrix< double, MEASUREMENT_SIZE, MEASUREMENT_SIZE > |
R |
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) |
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inline |
correction - a step that includes the innovation, gain and update steps into a single step p - observation h - observation model function J_H - jacobian observation model R - measurement noise covariance matrix
innovation
innovation covariance matrix
kalman gain