Struct bayes_estimate::estimators::information_root::InformationRootState

pub struct InformationRootState<N: RealField, D: Dim>
where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
{
    pub r: OVector<N, D>,
    pub R: OMatrix<N, D, D>,
}

Information State.

Linear representation as a information root state vector and the information root (upper triangular) matrix. For a given KalmanState the information root state inverse(R).inverse(R)’ == X, r == R.x For a given InformationState the information root state R’.R == I, r == inverse(R).i

Fields

r: OVector<N, D>

Information root state vector

R: OMatrix<N, D, D>

Information root matrix (upper triangular)

Implementations

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

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

pub fn information_state(&self) -> Result<InformationState<N, D>, &str>

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

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

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

where D: DimAdd<QD>, DefaultAllocator: Allocator<N, DimSum<D, QD>, DimSum<D, QD>> + Allocator<N, DimSum<D, QD>> + Allocator<N, D, QD> + Allocator<N, QD> + Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>> + Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>, DimSum<D, QD>>, DimSum<D, QD>: DimMin<DimSum<D, QD>>,

pub fn predict_inv_model<QD: Dim>( &mut self, x_pred: &OVector<N, D>, fx_inv: &OMatrix<N, D, D>, noise: &CoupledNoise<N, D, QD> ) -> Result<N, &str>

where D: DimAdd<QD>, DefaultAllocator: Allocator<N, DimSum<D, QD>, DimSum<D, QD>> + Allocator<N, DimSum<D, QD>> + Allocator<N, D, QD> + Allocator<N, QD> + Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>> + Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>, DimSum<D, QD>>, DimSum<D, QD>: DimMin<DimSum<D, QD>>,

pub fn observe_info<ZD: Dim>( &mut self, z: &OVector<N, ZD>, hx: &OMatrix<N, ZD, D>, noise_inv: &OMatrix<N, ZD, ZD> ) -> Result<(), &str>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, ZD, D> + Allocator<N, ZD, ZD> + Allocator<N, D> + Allocator<N, ZD> + Allocator<N, DimSum<D, ZD>, DimSum<D, U1>> + Allocator<N, DimSum<D, ZD>> + Allocator<N, DimMinimum<DimSum<D, ZD>, DimSum<D, U1>>> + Allocator<N, DimMinimum<DimSum<D, ZD>, DimSum<D, U1>>, DimSum<D, U1>>, D: DimAdd<ZD> + DimAdd<U1>, DimSum<D, ZD>: DimMin<DimSum<D, U1>>,

Trait Implementations

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

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

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<N: Copy + RealField, D: Dim> Estimator<N, D> for InformationRootState<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: Copy + RealField, D, ZD: Dim> ExtendedLinearObserver<N, D, ZD> for InformationRootState<N, D>

where DefaultAllocator: Allocator<N, D, D> + Allocator<N, ZD, D> + Allocator<N, ZD, ZD> + Allocator<N, D> + Allocator<N, ZD> + Allocator<N, DimSum<D, ZD>, DimSum<D, U1>> + Allocator<N, DimSum<D, ZD>> + Allocator<N, DimMinimum<DimSum<D, ZD>, DimSum<D, U1>>> + Allocator<N, DimMinimum<DimSum<D, ZD>, DimSum<D, U1>>, DimSum<D, U1>>, D: DimAdd<ZD> + DimAdd<U1> + Dim, DimSum<D, ZD>: DimMin<DimSum<D, U1>>,

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

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

fn eq(&self, other: &InformationRootState<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<InformationState<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: InformationState<N, D>) -> Result<Self, Self::Error>

Performs the conversion.

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> StructuralPartialEq for InformationRootState<N, D>

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