Struct bayes_estimate::estimators::information_root::InformationRootState [−][src]
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>, }
Expand description
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: 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>,
impl<N: Copy + RealField, D: Dim> InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + RealField, D: Dim> InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
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<(), &'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: &OVector<N, D>,
fx_inv: &OMatrix<N, D, 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: &OVector<N, ZD>,
hx: &OMatrix<N, ZD, D>,
noise_inv: &OMatrix<N, ZD, 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 + RealField, D: Clone + Dim> Clone for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Clone + RealField, D: Clone + Dim> Clone for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + RealField, D: Dim> Estimator<N, D> for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + RealField, D: Dim> Estimator<N, D> for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + 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>>,
impl<N: Copy + 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: &OVector<N, ZD>,
hx: &OMatrix<N, ZD, D>,
noise: &CorrelatedNoise<N, ZD>
) -> Result<(), &'static str>
fn observe_innovation(
&mut self,
s: &OVector<N, ZD>,
hx: &OMatrix<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: Copy + RealField, D: Dim> KalmanEstimator<N, D> for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + RealField, D: Dim> KalmanEstimator<N, D> for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
Initialise the estimator with a KalmanState.
The estimator’s estimate of the system’s KalmanState.
impl<N: PartialEq + RealField, D: PartialEq + Dim> PartialEq<InformationRootState<N, D>> for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: PartialEq + RealField, D: PartialEq + Dim> PartialEq<InformationRootState<N, D>> for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
This method tests for !=
.
impl<N: RealField, D: Dim> StructuralPartialEq for InformationRootState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
Auto Trait Implementations
impl<N, D> !RefUnwindSafe for InformationRootState<N, D>
impl<N, D> !Send for InformationRootState<N, D>
impl<N, D> !Sync for InformationRootState<N, D>
impl<N, D> !Unpin for InformationRootState<N, D>
impl<N, D> !UnwindSafe for InformationRootState<N, D>
Blanket Implementations
Mutably borrows from an owned value. Read more
type Output = T
type Output = T
Should always be Self
The inverse inclusion map: attempts to construct self
from the equivalent element of its
superset. Read more
pub fn is_in_subset(&self) -> bool
pub fn is_in_subset(&self) -> bool
Checks if self
is actually part of its subset T
(and can be converted to it).
pub fn to_subset_unchecked(&self) -> SS
pub fn to_subset_unchecked(&self) -> SS
Use with care! Same as self.to_subset
but without any property checks. Always succeeds.
pub fn from_subset(element: &SS) -> SP
pub fn from_subset(element: &SS) -> SP
The inclusion map: converts self
to the equivalent element of its superset.