Struct tracking::observation_model_2d::ObservationModel2D
source · pub struct ObservationModel2D<R: RealField + Copy> { /* private fields */ }Implementations§
Trait Implementations§
source§impl<R: RealField + Copy> ObservationModel<R, Const<4>, Const<2>> for ObservationModel2D<R>
impl<R: RealField + Copy> ObservationModel<R, Const<4>, Const<2>> for ObservationModel2D<R>
source§fn predict_observation(&self, state: &OVector<R, U4>) -> OVector<R, U2>
fn predict_observation(&self, state: &OVector<R, U4>) -> OVector<R, U2>
For a given state, predict the observation. Read more
source§fn update(
&self,
prior: &StateAndCovariance<R, SS>,
observation: &Matrix<R, OS, Const<1>, <DefaultAllocator as Allocator<R, OS>>::Buffer>,
covariance_method: CovarianceUpdateMethod
) -> Result<StateAndCovariance<R, SS>, Error>
fn update( &self, prior: &StateAndCovariance<R, SS>, observation: &Matrix<R, OS, Const<1>, <DefaultAllocator as Allocator<R, OS>>::Buffer>, covariance_method: CovarianceUpdateMethod ) -> Result<StateAndCovariance<R, SS>, Error>
Given prior state and observation, estimate the posterior state. Read more
source§fn observation_matrix(
&self
) -> &Matrix<R, OS, SS, <DefaultAllocator as Allocator<R, OS, SS>>::Buffer>
fn observation_matrix( &self ) -> &Matrix<R, OS, SS, <DefaultAllocator as Allocator<R, OS, SS>>::Buffer>
👎Deprecated since 0.8.0: Please use the H function instead
Get the observation matrix,
H.source§fn observation_matrix_transpose(
&self
) -> &Matrix<R, SS, OS, <DefaultAllocator as Allocator<R, SS, OS>>::Buffer>
fn observation_matrix_transpose( &self ) -> &Matrix<R, SS, OS, <DefaultAllocator as Allocator<R, SS, OS>>::Buffer>
👎Deprecated since 0.8.0: Please use the HT function instead
Get the transpose of the observation matrix,
HT.source§fn observation_noise_covariance(
&self
) -> &Matrix<R, OS, OS, <DefaultAllocator as Allocator<R, OS, OS>>::Buffer>
fn observation_noise_covariance( &self ) -> &Matrix<R, OS, OS, <DefaultAllocator as Allocator<R, OS, OS>>::Buffer>
👎Deprecated since 0.8.0: Please use the R function instead
Get the observation noise covariance,
R.source§fn evaluate(
&self,
state: &Matrix<R, SS, Const<1>, <DefaultAllocator as Allocator<R, SS>>::Buffer>
) -> Matrix<R, OS, Const<1>, <DefaultAllocator as Allocator<R, OS>>::Buffer>
fn evaluate( &self, state: &Matrix<R, SS, Const<1>, <DefaultAllocator as Allocator<R, SS>>::Buffer> ) -> Matrix<R, OS, Const<1>, <DefaultAllocator as Allocator<R, OS>>::Buffer>
👎Deprecated since 0.8.0: Please use the predict_observation function instead
For a given state, predict the observation. Read more
Auto Trait Implementations§
impl<R> Freeze for ObservationModel2D<R>where
R: Freeze,
impl<R> RefUnwindSafe for ObservationModel2D<R>where
R: RefUnwindSafe,
impl<R> Send for ObservationModel2D<R>
impl<R> Sync for ObservationModel2D<R>
impl<R> Unpin for ObservationModel2D<R>where
R: Unpin,
impl<R> UnwindSafe for ObservationModel2D<R>where
R: UnwindSafe,
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
source§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
source§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 moresource§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).source§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.source§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self to the equivalent element of its superset.