Expand description
Kalman filter and Rauch-Tung-Striebel smoothing implementation
Characteristics:
- Uses the nalgebra crate for math.
- Supports
no_stdto facilitate running on embedded microcontrollers. - Includes various methods of computing the covariance matrix on the update step.
- Examples included.
- Strong typing used to ensure correct matrix dimensions at compile time.
Throughout the library, the generic type SS means “state size” and OS is
“observation size”. These refer to the number of dimensions of the state
vector and observation vector, respectively.
Structs§
- Kalman
Filter NoControl - A Kalman filter with no control inputs, a linear process model and linear observation model
- State
AndCovariance - State and covariance pair for a given estimate
Enums§
- Covariance
Update Method - Specifies the approach used for updating the covariance matrix
- Error
- An Kalman filter error
Traits§
- Observation
Model - An observation model, potentially non-linear.
- Transition
Model Linear NoControl - A linear model of process dynamics with no control inputs