Struct bayes_estimate::models::InformationState
source · pub struct InformationState<N: RealField, D: Dim>{
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§
source§impl<N: Copy + RealField, D: Dim> InformationState<N, D>
impl<N: Copy + RealField, D: Dim> InformationState<N, D>
sourcepub fn predict_linear<QD: Dim>(
&mut self,
pred_inv: OMatrix<N, D, D>,
noise: &CoupledNoise<N, D, QD>
) -> Result<N, &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, &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 add_information(&mut self, information: &InformationState<N, D>)
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§
source§impl<N: Clone + RealField, D: Clone + Dim> Clone for InformationState<N, D>
impl<N: Clone + RealField, D: Clone + Dim> Clone for InformationState<N, D>
source§fn clone(&self) -> InformationState<N, D>
fn clone(&self) -> InformationState<N, D>
Returns a copy of the value. Read more
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from
source
. Read moresource§impl<N: RealField, D: Dim> Estimator<N, D> for InformationState<N, D>
impl<N: RealField, D: Dim> Estimator<N, D> for InformationState<N, D>
source§impl<N: Copy + RealField, D: Dim, ZD: Dim> ExtendedLinearObserver<N, D, ZD> for InformationState<N, D>
impl<N: Copy + RealField, D: Dim, ZD: Dim> ExtendedLinearObserver<N, D, ZD> for InformationState<N, D>
source§fn observe_innovation(
&mut self,
s: &OVector<N, ZD>,
hx: &OMatrix<N, ZD, D>,
noise: &CorrelatedNoise<N, ZD>
) -> Result<(), &str>
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.
source§impl<N: RealField, D: Dim> ExtendedLinearPredictor<N, D> for InformationState<N, D>
impl<N: RealField, D: Dim> ExtendedLinearPredictor<N, D> for InformationState<N, D>
source§impl<N: RealField, D: Dim> KalmanEstimator<N, D> for InformationState<N, D>
impl<N: RealField, D: Dim> KalmanEstimator<N, D> for InformationState<N, D>
source§fn kalman_state(&self) -> Result<KalmanState<N, D>, &str>
fn kalman_state(&self) -> Result<KalmanState<N, D>, &str>
The estimator’s estimate of the system’s KalmanState.
source§impl<N: PartialEq + RealField, D: PartialEq + Dim> PartialEq for InformationState<N, D>
impl<N: PartialEq + RealField, D: PartialEq + Dim> PartialEq for InformationState<N, D>
source§fn eq(&self, other: &InformationState<N, D>) -> bool
fn eq(&self, other: &InformationState<N, D>) -> bool
This method tests for
self
and other
values to be equal, and is used
by ==
.source§impl<N: Copy + RealField, D: Dim> TryFrom<InformationState<N, D>> for InformationRootState<N, D>
impl<N: Copy + RealField, D: Dim> TryFrom<InformationState<N, D>> for InformationRootState<N, D>
source§impl<N: RealField, D: Dim> TryFrom<KalmanState<N, D>> for InformationState<N, D>
impl<N: RealField, D: Dim> TryFrom<KalmanState<N, D>> for InformationState<N, D>
impl<N: RealField, D: Dim> StructuralPartialEq for InformationState<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§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self
from the equivalent element of its
superset. Read more§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if
self
is actually part of its subset T
(and can be converted to it).§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset
but without any property checks. Always succeeds.§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self
to the equivalent element of its superset.