Trait nyx_space::od::estimate::Estimate[−][src]

pub trait Estimate<T: State> where
    Self: Clone + PartialEq + Sized + Display,
    DefaultAllocator: Allocator<f64, <T as State>::Size> + Allocator<f64, <T as State>::Size, <T as State>::Size>, {
    fn zeros(state: T) -> Self;

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

    fn nominal_state(&self) -> T;

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

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

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

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

    fn predicted(&self) -> bool;

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

    fn epoch_fmt(&self) -> EpochFormat;

    fn covar_fmt(&self) -> CovarFormat;

    fn epoch(&self) -> Epoch { ... }

    fn set_epoch(&mut self, dt: Epoch) { ... }

    fn state(&self) -> T { ... }

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

    fn within_3sigma(&self) -> bool { ... }

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

    fn default_header() -> Vec<String> { ... }

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

Stores an Estimate, as the result of a time_update or measurement_update.

Required methods

fn zeros(state: T) -> Self[src]

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

fn state_deviation(&self) -> VectorN<f64, <T as State>::Size>[src]

The state deviation as computed by the filter.

fn nominal_state(&self) -> T[src]

The nominal state as reported by the filter dynamics

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

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>[src]

The predicted covariance of this estimate from the time update

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

Sets the state deviation.

fn set_covar(
    &mut self,
    new_covar: MatrixMN<f64, <T as State>::Size, <T as State>::Size>
)[src]

Sets the Covariance of this estimate

fn predicted(&self) -> bool[src]

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>[src]

The STM used to compute this Estimate

fn epoch_fmt(&self) -> EpochFormat[src]

The Epoch format upon serialization

fn covar_fmt(&self) -> CovarFormat[src]

The covariance format upon serialization

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Provided methods

fn epoch(&self) -> Epoch[src]

Epoch of this Estimate

fn set_epoch(&mut self, dt: Epoch)[src]

fn state(&self) -> T[src]

The estimated state

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

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[src]

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>[src]

Returns the header

fn default_header() -> Vec<String>[src]

Returns the default header

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

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

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Implementors

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>, [src]

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