Struct bayes_estimate::estimators::information_root::InformationRootState

pub struct InformationRootState<N: SimdRealField, D: Dim> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,  {
    pub r: VectorN<N, D>,
    pub R: MatrixN<N, 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 == invserse(R).i

Fields

r: VectorN<N, D>

Information root state vector

R: MatrixN<N, D>

Information root matrix (upper triangular)

Implementations

impl<N: RealField, D: Dim> InformationRootState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

pub fn new_zero(d: D) -> InformationRootState<N, D>

pub fn init_information(
    &mut self,
    state: &InformationState<N, D>
) -> Result<N, &'static str>

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

impl<N: RealField, D: Dim> InformationRootState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

pub fn predict<QD: Dim>(
    &mut self,
    x_pred: &VectorN<N, D>,
    fx: &MatrixN<N, D>,
    noise: &CoupledNoise<N, D, QD>
) -> Result<(), &'static 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>,
    DimSum<D, QD>: DimMin<DimSum<D, QD>>,
    DefaultAllocator: Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>> + Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>, DimSum<D, QD>>, 

pub fn predict_inv_model<QD: Dim>(
    &mut self,
    x_pred: &VectorN<N, D>,
    fx_inv: &MatrixN<N, D>,
    noise: &CoupledNoise<N, D, QD>
) -> Result<N, &'static 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>,
    DimSum<D, QD>: DimMin<DimSum<D, QD>>,
    DefaultAllocator: Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>> + Allocator<N, DimMinimum<DimSum<D, QD>, DimSum<D, QD>>, DimSum<D, QD>>, 

pub fn observe_info<ZD: Dim>(
    &mut self,
    z: &VectorN<N, ZD>,
    hx: &MatrixMN<N, ZD, D>,
    noise_inv: &MatrixN<N, ZD>
) -> Result<(), &'static str> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, ZD, D> + Allocator<N, ZD, ZD> + Allocator<N, D> + Allocator<N, ZD>,
    D: DimAdd<ZD> + DimAdd<U1>,
    DefaultAllocator: Allocator<N, DimSum<D, ZD>, DimSum<D, U1>> + Allocator<N, DimSum<D, ZD>>,
    DimSum<D, ZD>: DimMin<DimSum<D, U1>>,
    DefaultAllocator: Allocator<N, DimMinimum<DimSum<D, ZD>, DimSum<D, U1>>> + Allocator<N, DimMinimum<DimSum<D, ZD>, DimSum<D, U1>>, DimSum<D, U1>>, 

Trait Implementations

impl<N: Clone + SimdRealField, 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.

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

Performs copy-assignment from source.

impl<N: RealField, D: Dim> Estimator<N, D> for InformationRootState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

fn state(&self) -> Result<VectorN<N, D>, &'static str>

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

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

fn observe_innovation(
    &mut self,
    s: &VectorN<N, ZD>,
    hx: &MatrixMN<N, ZD, D>,
    noise: &CorrelatedNoise<N, ZD>
) -> Result<(), &'static str>

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

impl<N: RealField, D: Dim> KalmanEstimator<N, D> for InformationRootState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

fn init(&mut self, state: &KalmanState<N, D>) -> Result<(), &'static str>

Initialise the estimator with a KalmanState.

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

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

impl<N: PartialEq + SimdRealField, D: PartialEq + Dim> PartialEq<InformationRootState<N, D>> 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: &InformationRootState<N, D>) -> bool

This method tests for !=.

impl<N: SimdRealField, D: Dim> StructuralPartialEq for InformationRootState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,