Struct bayes_estimate::models::KalmanState

pub struct KalmanState<N: RealField, D: Dim>
where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
{
    pub x: OVector<N, D>,
    pub X: OMatrix<N, D, D>,
}

Kalman state.

Linear representation as a state vector and the state covariance (symmetric positive semi-definite) matrix.

Fields

x: OVector<N, D>

State vector

X: OMatrix<N, D, D>

State covariance matrix (symmetric positive semi-definite)

Implementations

impl<N: Copy + FromPrimitive + RealField, D: Dim> KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

pub fn try_from(state: KalmanState<N, D>) -> Result<Self, &'static str>

impl<N: Copy + FromPrimitive + RealField, D: Dim> KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> + Allocator<N, U1, D>,

pub fn kalman_state(UU: &Vec<OVector<N, D>>, kappa: N) -> KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> + Allocator<N, U1, D>,

Calculates the Kalman State from the ‘UU’ unscented duplex sigma points.

pub fn to_unscented_duplex( &self, scale: N ) -> Result<(Vec<OVector<N, D>>, N), &'static str>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

Calculates the unscented duplex sigma points from a Kalman State.

Will return an error if the covariance matrix is not PSD.

Trait Implementations

impl<N: Clone + RealField, D: Clone + Dim> Clone for KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

fn clone(&self) -> KalmanState<N, D>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<N: RealField, D: Dim> Estimator<N, D> for KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

fn state(&self) -> Result<OVector<N, D>, &str>

The estimator’s estimate of the system’s state.

impl<N: RealField, D: Dim, ZD: Dim> ExtendedLinearObserver<N, D, ZD> for KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, ZD, ZD> + Allocator<N, ZD, D> + Allocator<N, D, ZD> + Allocator<N, D> + Allocator<N, ZD>,

fn observe_innovation( &mut self, s: &OVector<N, ZD>, hx: &OMatrix<N, ZD, D>, noise: &CorrelatedNoise<N, ZD> ) -> Result<(), &str>

Uses a non-linear state observation with linear estimation model and additive noise.

impl<N: RealField, D: Dim> ExtendedLinearPredictor<N, D> for KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

fn predict( &mut self, x_pred: &OVector<N, D>, fx: &OMatrix<N, D, D>, noise: &CorrelatedNoise<N, D> ) -> Result<(), &str>

Uses a non-linear state prediction with linear estimation model and additive noise.

impl<N: Copy + FromPrimitive + RealField, D: Dim> From<KalmanState<N, D>> for UnscentedDuplexState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> + Allocator<N, U1, D>,

fn from(state: KalmanState<N, D>) -> Self

Converts to this type from the input type.

impl<N: Copy + RealField, D: Dim> KalmanEstimator<N, D> for KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

fn kalman_state(&self) -> Result<KalmanState<N, D>, &str>

The estimator’s estimate of the system’s KalmanState.

impl<N: PartialEq + RealField, D: PartialEq + Dim> PartialEq for KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

fn eq(&self, other: &KalmanState<N, D>) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<N: Copy + RealField, D: Dim> TryFrom<KalmanState<N, D>> for InformationRootState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

type Error = &'static str

The type returned in the event of a conversion error.

fn try_from(state: KalmanState<N, D>) -> Result<Self, Self::Error>

Performs the conversion.

impl<N: RealField, D: Dim> TryFrom<KalmanState<N, D>> for InformationState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

type Error = &'static str

The type returned in the event of a conversion error.

fn try_from(state: KalmanState<N, D>) -> Result<Self, Self::Error>

Performs the conversion.

impl<N: Copy + RealField, D: Dim> TryFrom<KalmanState<N, D>> for UDState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,

fn try_from(state: KalmanState<N, D>) -> Result<Self, Self::Error>

Construct the UDState with a KalmanState.

The covariance matrix X is factorised into a U.d.U’ as a UD matrix.

type Error = &'static str

The type returned in the event of a conversion error.

impl<N: RealField, D: Dim> StructuralPartialEq for KalmanState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,