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>, {

Show methods    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 { ... }

}
Expand description

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

Required methods

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

The state deviation as computed by the filter.

The nominal state as reported by the filter dynamics

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

The predicted covariance of this estimate from the time update

Sets the state deviation.

Sets the Covariance of this estimate

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

The STM used to compute this Estimate

The Epoch format upon serialization

The covariance format upon serialization

Provided methods

Epoch of this Estimate

The estimated state

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

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

Returns the header

Returns the default header

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

Implementors