Struct bayes_estimate::models::KalmanState

pub struct KalmanState<N: RealField, D: Dim> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,  {
    pub x: OVector<N, D>,
    pub X: OMatrix<N, D, D>,
}

Kalman state.

Linear representation as a state vector and the state covariance (symmetric positive semi-definite) matrix.

Fields

x: OVector<N, D>

State vector

X: OMatrix<N, D, D>

State covariance matrix (symmetric positive semi-definite)

Implementations

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

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

impl<N: Copy + FromPrimitive + RealField, D: Dim> KalmanState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> + Allocator<N, U1, D>, 

pub fn predict_unscented(
    &mut self,
    f: fn(_: &OVector<N, D>) -> OVector<N, D>,
    noise: &CorrelatedNoise<N, D>,
    kappa: N
) -> Result<(), &'static str>

State prediction with a functional prediction model and additive noise.

pub fn observe_unscented<ZD: Dim>(
    &mut self,
    h: fn(_: &OVector<N, D>) -> OVector<N, ZD>,
    h_normalise: fn(_: &mut OVector<N, ZD>, _: &OVector<N, ZD>),
    noise: &CorrelatedNoise<N, ZD>,
    s: &OVector<N, ZD>,
    kappa: N
) -> Result<(), &'static str> where
    DefaultAllocator: Allocator<N, D, ZD> + Allocator<N, ZD, ZD> + Allocator<N, U1, ZD> + Allocator<N, ZD>, 

Trait Implementations

impl<N: Clone + RealField, D: Clone + Dim> Clone for KalmanState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

fn clone(&self) -> KalmanState<N, D>

Returns a copy of the value.

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

Performs copy-assignment from source.

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

fn state(&self) -> Result<OVector<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 KalmanState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, ZD, ZD> + Allocator<N, ZD, D> + Allocator<N, D, 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>

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

impl<N: RealField, D: Dim> ExtendedLinearPredictor<N, D> for KalmanState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

fn predict(
    &mut self,
    x_pred: &OVector<N, D>,
    fx: &OMatrix<N, D, D>,
    noise: &CorrelatedNoise<N, D>
) -> Result<(), &'static str>

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

impl<N: Copy + RealField, D: Dim> KalmanEstimator<N, D> for KalmanState<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 + RealField, D: PartialEq + Dim> PartialEq<KalmanState<N, D>> for KalmanState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>, 

fn eq(&self, other: &KalmanState<N, D>) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &KalmanState<N, D>) -> bool

This method tests for !=.

impl<N: RealField, D: Dim> StructuralPartialEq for KalmanState<N, D> where
    DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,