Struct nyx_space::od::estimate::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<usize, <T as State>::Size, <T as State>::Size>,  {
    pub nominal_state: T,
    pub state_deviation: VectorN<f64, <T as State>::Size>,
    pub covar: MatrixMN<f64, <T as State>::Size, <T as State>::Size>,
    pub covar_bar: MatrixMN<f64, <T as State>::Size, <T as State>::Size>,
    pub predicted: bool,
    pub stm: MatrixMN<f64, <T as State>::Size, <T as State>::Size>,
    pub epoch_fmt: EpochFormat,
    pub covar_fmt: CovarFormat,
}

Kalman filter Estimate

Fields

nominal_state: T

The estimated state

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

The state deviation

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

The Covariance of this estimate

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

The STM used to compute this Estimate

epoch_fmt: EpochFormat

The Epoch format upon serialization

covar_fmt: CovarFormat

The covariance format upon serialization

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<usize, <T as State>::Size, <T as State>::Size>, 

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

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<usize, <T as State>::Size, <T as State>::Size>, 

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

Returns a copy of the value.

pub 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<usize, <T as State>::Size, <T as State>::Size>, 

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<usize, <T as State>::Size, <T as State>::Size>, 

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<usize, <T as State>::Size, <T as State>::Size>, 

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) -> VectorN<f64, <T as State>::Size>

The state deviation as computed by the filter.

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

The STM used to compute this Estimate

fn epoch_fmt(&self) -> EpochFormat

The Epoch format upon serialization

fn covar_fmt(&self) -> CovarFormat

The covariance format upon serialization

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

Sets the state deviation.

fn set_covar(
    &mut self,
    new_covar: MatrixMN<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

fn header(epoch_fmt: EpochFormat, covar_fmt: CovarFormat) -> Vec<String>

Returns the header

fn default_header() -> Vec<String>

Returns the default header

fn covar_ij(&self, i: usize, j: usize) -> f64

Returns the covariance element at position (i, j) formatted with this estimate’s covariance formatter

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<usize, <T as State>::Size, <T as State>::Size>, 

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

Formats the value using the given formatter.

impl NavSolution<Orbit> for KfEstimate<Orbit>

fn orbital_state(&self) -> Orbit

fn expected_state(&self) -> Orbit

Returns the nominal state as computed by the dynamics

impl<T: PartialEq + State> PartialEq<KfEstimate<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<usize, <T as State>::Size, <T as State>::Size>, 

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: &KfEstimate<T>) -> bool

This method tests for !=.

impl<T: State> Serialize 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<usize, <T as State>::Size, <T as State>::Size>, 

fn serialize<O>(&self, serializer: O) -> Result<O::Ok, O::Error> where
    O: Serializer, 

Serializes the estimate

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<usize, <T as State>::Size, <T as State>::Size>,