Struct bayes_estimate::models::InformationState [−][src]
pub struct InformationState<N: RealField, D: Dim> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, { pub i: OVector<N, D>, pub I: OMatrix<N, D, D>, }
Expand description
Information state.
Linear representation as a information state vector and the information (symmetric positive semi-definite) matrix. For a given KalmanState the information state I == inverse(X), i == I.x
Fields
i: OVector<N, D>
Information state vector
I: OMatrix<N, D, D>
Information matrix (symmetric positive semi-definite)
Implementations
impl<N: RealField, D: Dim> InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: RealField, D: Dim> InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + RealField, D: Dim> InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + RealField, D: Dim> InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
pub fn predict_linear<QD: Dim>(
&mut self,
pred_inv: OMatrix<N, D, D>,
noise: &CoupledNoise<N, D, QD>
) -> Result<N, &'static str> where
DefaultAllocator: Allocator<N, QD, QD> + Allocator<N, D, QD> + Allocator<N, QD, D> + Allocator<N, QD>,
pub fn predict_linear<QD: Dim>(
&mut self,
pred_inv: OMatrix<N, D, D>,
noise: &CoupledNoise<N, D, QD>
) -> Result<N, &'static str> where
DefaultAllocator: Allocator<N, QD, QD> + Allocator<N, D, QD> + Allocator<N, QD, D> + Allocator<N, QD>,
Linear information predict.
The numerical solution takes particular care to avoid invertibility requirements for the noise. Therefore both zero noises q and zeros in the couplings G can be used.
pub fn observe_info<ZD: Dim>(
&self,
hx: &OMatrix<N, ZD, D>,
noise_inv: &OMatrix<N, ZD, ZD>,
z: &OVector<N, ZD>
) -> InformationState<N, D> where
DefaultAllocator: Allocator<N, ZD, ZD> + Allocator<N, ZD, D> + Allocator<N, D, ZD> + Allocator<N, ZD>,
Trait Implementations
impl<N: Clone + RealField, D: Clone + Dim> Clone for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Clone + RealField, D: Clone + Dim> Clone for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: RealField, D: Dim> Estimator<N, D> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: RealField, D: Dim> Estimator<N, D> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: Copy + RealField, D: Dim, ZD: Dim> ExtendedLinearObserver<N, D, ZD> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D, ZD> + Allocator<N, ZD, D> + Allocator<N, ZD, ZD> + Allocator<N, D> + Allocator<N, ZD>,
impl<N: Copy + RealField, D: Dim, ZD: Dim> ExtendedLinearObserver<N, D, ZD> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D, ZD> + Allocator<N, ZD, D> + Allocator<N, ZD, 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<(), &'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: RealField, D: Dim> ExtendedLinearPredictor<N, D> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: RealField, D: Dim> ExtendedLinearPredictor<N, D> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: RealField, D: Dim> KalmanEstimator<N, D> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: RealField, D: Dim> KalmanEstimator<N, D> for InformationState<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<InformationState<N, D>> for InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
impl<N: PartialEq + RealField, D: PartialEq + Dim> PartialEq<InformationState<N, D>> for InformationState<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 InformationState<N, D> where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
Auto Trait Implementations
impl<N, D> !RefUnwindSafe for InformationState<N, D>
impl<N, D> !Send for InformationState<N, D>
impl<N, D> !Sync for InformationState<N, D>
impl<N, D> !Unpin for InformationState<N, D>
impl<N, D> !UnwindSafe for InformationState<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.