structs.ekf_data

Settings of the extended Kalman filter.

EKFSettings

Settings of the extended Kalman filter.

The state is propagated with the nonlinear dynamics fx, the covariance with their Jacobian A.

create(A, H, Q, R, fx, hx) classmethod

Create the settings from the Jacobians, the noise covariances, and the model.

Parameters:

Name	Type	Description	Default
A	Array	The Jacobian of the continuous dynamics w.r.t. the state.	required
H	Array	The Jacobian of the observation function w.r.t. the state.	required
Q	Array	The process noise covariance.	required
R	Array	The measurement noise covariance.	required
fx	Callable[[Array, Array, Array, Array, Array, Array, Array | None, Array | None], tuple[Array, Array, Array, Array, Array | None]]	The continuous dynamics function.	required
hx	Callable[[Array, Array, Array, Array, Array, Array, Array | None, Array | None], Array]	The observation function.	required

Returns:

Type	Description
EKFSettings	The settings.

Source code in drone_estimators/structs/ekf_data.py

@classmethod
def create(
    cls,
    A: Array,
    H: Array,
    Q: Array,
    R: Array,
    fx: Callable[
        [Array, Array, Array, Array, Array, Array, Array | None, Array | None],
        tuple[Array, Array, Array, Array, Array | None],
    ],
    hx: Callable[[Array, Array, Array, Array, Array, Array, Array | None, Array | None], Array],
) -> EKFSettings:
    """Create the settings from the Jacobians, the noise covariances, and the model.

    Args:
        A: The Jacobian of the continuous dynamics w.r.t. the state.
        H: The Jacobian of the observation function w.r.t. the state.
        Q: The process noise covariance.
        R: The measurement noise covariance.
        fx: The continuous dynamics function.
        hx: The observation function.

    Returns:
        The settings.
    """
    return cls(A, H, Q, R, fx, hx)