Struct nyx_space::od::kalman::KF

pub struct KF<S, A, M, T> where
    S: DimName,
    A: DimName,
    M: DimName,
    T: EstimableState<S>,
    DefaultAllocator: Allocator<f64, M> + Allocator<f64, S> + Allocator<f64, M, M> + Allocator<f64, M, S> + Allocator<f64, S, S> + Allocator<f64, A, A> + Allocator<f64, S, A> + Allocator<f64, A, S>,  {
    pub prev_estimate: KfEstimate<S, T>,
    pub measurement_noise: MatrixMN<f64, M, M>,
    pub process_noise: Option<MatrixMN<f64, A, A>>,
    pub process_noise_dt: Option<f64>,
    pub ekf: bool,
    // some fields omitted
}

Defines both a Classical and an Extended Kalman filter (CKF and EKF)

Fields

prev_estimate: KfEstimate<S, T>

The previous estimate used in the KF computations.

measurement_noise: MatrixMN<f64, M, M>

Sets the Measurement noise (usually noted R)

process_noise: Option<MatrixMN<f64, A, A>>

Sets the process noise (usually noted Q) in the frame of the estimated state

process_noise_dt: Option<f64>

Enables state noise compensation (process noise) only be applied if the time between measurements is less than the process_noise_dt amount in seconds

ekf: bool

Determines whether this KF should operate as a Conventional/Classical Kalman filter or an Extended Kalman Filter. Recall that one should switch to an Extended KF only once the estimate is good (i.e. after a few good measurement updates on a CKF).

Implementations

impl<S, A, M, T> KF<S, A, M, T> where
    S: DimName,
    A: DimName,
    M: DimName,
    T: EstimableState<S>,
    DefaultAllocator: Allocator<f64, M> + Allocator<f64, S> + Allocator<f64, M, M> + Allocator<f64, M, S> + Allocator<f64, S, M> + Allocator<f64, S, S> + Allocator<f64, A, A> + Allocator<f64, S, A> + Allocator<f64, A, S>, 

pub fn new(
    initial_estimate: KfEstimate<S, T>,
    process_noise: MatrixMN<f64, A, A>,
    measurement_noise: MatrixMN<f64, M, M>,
    process_noise_dt: Option<f64>
) -> Self

Initializes this KF with an initial estimate and measurement noise.

impl<S, M, T> KF<S, U3, M, T> where
    S: DimName,
    M: DimName,
    T: EstimableState<S>,
    DefaultAllocator: Allocator<f64, M> + Allocator<f64, S> + Allocator<f64, M, M> + Allocator<f64, M, S> + Allocator<f64, S, M> + Allocator<f64, S, S> + Allocator<f64, U3, U3> + Allocator<f64, S, U3> + Allocator<f64, U3, S>, 

pub fn no_snc(
    initial_estimate: KfEstimate<S, T>,
    measurement_noise: MatrixMN<f64, M, M>
) -> Self

Initializes this KF without SNC

Trait Implementations

impl<S: Clone, A: Clone, M: Clone, T: Clone> Clone for KF<S, A, M, T> where
    S: DimName,
    A: DimName,
    M: DimName,
    T: EstimableState<S>,
    DefaultAllocator: Allocator<f64, M> + Allocator<f64, S> + Allocator<f64, M, M> + Allocator<f64, M, S> + Allocator<f64, S, S> + Allocator<f64, A, A> + Allocator<f64, S, A> + Allocator<f64, A, S>, 

fn clone(&self) -> KF<S, A, M, T>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<S: Debug, A: Debug, M: Debug, T: Debug> Debug for KF<S, A, M, T> where
    S: DimName,
    A: DimName,
    M: DimName,
    T: EstimableState<S>,
    DefaultAllocator: Allocator<f64, M> + Allocator<f64, S> + Allocator<f64, M, M> + Allocator<f64, M, S> + Allocator<f64, S, S> + Allocator<f64, A, A> + Allocator<f64, S, A> + Allocator<f64, A, S>, 

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl<S, A, M, T> Filter<S, A, M, T> for KF<S, A, M, T> where
    S: DimName,
    A: DimName,
    M: DimName,
    T: EstimableState<S>,
    DefaultAllocator: Allocator<f64, M> + Allocator<f64, S> + Allocator<f64, M, M> + Allocator<f64, M, S> + Allocator<f64, S, M> + Allocator<f64, S, S> + Allocator<f64, A, A> + Allocator<f64, S, A> + Allocator<f64, A, S>, 

type Estimate = KfEstimate<S, T>

fn previous_estimate(&self) -> &Self::Estimate

Returns the previous estimate

fn set_previous_estimate(&mut self, est: &Self::Estimate)

Set the previous estimate

fn update_stm(&mut self, new_stm: MatrixMN<f64, S, S>)

Update the State Transition Matrix (STM). This function must be called in between each call to time_update or measurement_update.

fn update_h_tilde(&mut self, h_tilde: MatrixMN<f64, M, S>)

Update the sensitivity matrix (or "H tilde"). This function must be called prior to each call to measurement_update.

fn time_update(
    &mut self,
    nominal_state: T
) -> Result<Self::Estimate, FilterError>

Computes a time update/prediction (i.e. advances the filter estimate with the updated STM).

May return a FilterError if the STM was not updated.

fn measurement_update(
    &mut self,
    nominal_state: T,
    real_obs: VectorN<f64, M>,
    computed_obs: VectorN<f64, M>
) -> Result<(Self::Estimate, Residual<M>), FilterError>

Computes the measurement update with a provided real observation and computed observation.

May return a FilterError if the STM or sensitivity matrices were not updated.

fn is_extended(&self) -> bool

Returns whether the filter is an extended filter (e.g. EKF)

fn set_extended(&mut self, status: bool)

Sets the filter to be extended or not depending on the value of status

fn set_process_noise(&mut self, prc: MatrixMN<f64, A, A>)

Sets the process noise matrix in the frame of the estimated state