Struct nyx_space::od::estimate::kfestimate::KfEstimate

pub struct KfEstimate<T: State>
where
    DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>,
    <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy,
    <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,
{
    pub nominal_state: T,
    pub state_deviation: OVector<f64, <T as State>::Size>,
    pub covar: OMatrix<f64, <T as State>::Size, <T as State>::Size>,
    pub covar_bar: OMatrix<f64, <T as State>::Size, <T as State>::Size>,
    pub predicted: bool,
    pub stm: OMatrix<f64, <T as State>::Size, <T as State>::Size>,
}

Kalman filter Estimate

Fields

nominal_state: T

The estimated state

state_deviation: OVector<f64, <T as State>::Size>

The state deviation

covar: OMatrix<f64, <T as State>::Size, <T as State>::Size>

The Covariance of this estimate

covar_bar: OMatrix<f64, <T as State>::Size, <T as State>::Size>

The predicted covariance of this estimate

predicted: bool

Whether or not this is a predicted estimate from a time update, or an estimate from a measurement

stm: OMatrix<f64, <T as State>::Size, <T as State>::Size>

The STM used to compute this Estimate

Implementations

impl<T: State> KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

pub fn from_covar( nominal_state: T, covar: OMatrix<f64, <T as State>::Size, <T as State>::Size> ) -> Self

Initializes a new filter estimate from the nominal state (not dispersed) and the full covariance

pub fn from_diag( nominal_state: T, diag: OVector<f64, <T as State>::Size> ) -> Self

Initializes a new filter estimate from the nominal state (not dispersed) and the diagonal of the covariance

impl KfEstimate<Spacecraft>

pub fn disperse_from_diag( nominal_state: Spacecraft, params: &[(StateParameter, f64)], seed: Option<u128> ) -> Self

Generates an initial Kalman filter state estimate dispersed from the nominal state using the provided standard deviation parameters.

The resulting estimate will have a diagonal covariance matrix constructed from the variances of each parameter. Limitation: This method incorrectly assumes all parameters are statistically independent.

pub fn to_random_variable( &self ) -> Result<MultivariateNormal<Spacecraft>, NyxError>

Builds a multivariate random variable from this estimate’s nominal state and covariance, zero mean.

pub fn sigma_for(&self, param: StateParameter) -> Result<f64, AstroError>

Returns the 1-sigma uncertainty for a given parameter, in that parameter’s unit

This method uses the OrbitDual structure to compute the estimate in the hyperdual space and rotate the nominal covariance into that space.

pub fn keplerian_covar(&self) -> SMatrix<f64, 6, 6>

Returns the 6x6 covariance (i.e. square of the sigma/uncertainty) of the SMA, ECC, INC, RAAN, AOP, and True Anomaly.

Trait Implementations

impl<T: Clone + State> Clone for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

fn clone(&self) -> KfEstimate<T>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<T: Debug + State> Debug for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T: State> Display for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T: State> Estimate<T> for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

fn zeros(nominal_state: T) -> Self

An empty estimate. This is useful if wanting to store an estimate outside the scope of a filtering loop.

fn nominal_state(&self) -> T

The nominal state as reported by the filter dynamics

fn state_deviation(&self) -> OVector<f64, <T as State>::Size>

The state deviation as computed by the filter.

fn covar(&self) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>

The Covariance of this estimate. Will return the predicted covariance if this is a time update/prediction.

fn predicted_covar( &self ) -> OMatrix<f64, <T as State>::Size, <T as State>::Size>

The predicted covariance of this estimate from the time update

fn predicted(&self) -> bool

Whether or not this is a predicted estimate from a time update, or an estimate from a measurement

fn stm(&self) -> &OMatrix<f64, <T as State>::Size, <T as State>::Size>

The STM used to compute this Estimate

fn set_state_deviation(&mut self, new_state: OVector<f64, <T as State>::Size>)

Sets the state deviation.

fn set_covar( &mut self, new_covar: OMatrix<f64, <T as State>::Size, <T as State>::Size> )

Sets the Covariance of this estimate

fn epoch(&self) -> Epoch

Epoch of this Estimate

fn set_epoch(&mut self, dt: Epoch)

fn state(&self) -> T

The estimated state

fn within_sigma(&self, sigma: f64) -> bool

Returns whether this estimate is within some bound The 68-95-99.7 rule is a good way to assess whether the filter is operating normally

fn within_3sigma(&self) -> bool

Returns whether this estimate is within 3 sigma, which represent 99.7% for a Normal distribution

impl<T: State> LowerExp for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl NavSolution<Spacecraft> for KfEstimate<Spacecraft>

fn orbital_state(&self) -> Orbit

fn expected_state(&self) -> Orbit

Returns the nominal state as computed by the dynamics

impl<T: PartialEq + State> PartialEq for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

fn eq(&self, other: &KfEstimate<T>) -> bool

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

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<T: Copy + State> Copy for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,

impl<T: State> StructuralPartialEq for KfEstimate<T>

where DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size> + Allocator<usize, <T as State>::Size> + Allocator<f64, <T as State>::VecLength> + Allocator<usize, <T as State>::Size, <T as State>::Size>, <DefaultAllocator as Allocator<f64, <T as State>::Size>>::Buffer: Copy, <DefaultAllocator as Allocator<f64, <T as State>::Size, <T as State>::Size>>::Buffer: Copy,