Struct GaussMarkov

pub struct GaussMarkov {
    pub tau: Duration,
    pub process_noise: f64,
    pub prev_epoch: Option<Epoch>,
    pub init_sample: Option<f64>,
}

A first order Gauss-Markov process for modeling biases as described in section 5.2.4 of the NASA Best Practices for Navigation Filters (D’Souza et al.).

The process is defined by the following stochastic differential equation:

\dot{b(t)} = -1/τ * b(t) + w(t)

Programmatically, it’s calculated by sampling from b(t) ~ 𝓝(0, p_b(t)), where

p_b(t) = exp((-2 / τ) * (t - t_0)) * p_b(t_0) + s(t - t_0)

s(t - t_0) = ((q * τ) / 2) * (1 - exp((-2 / τ) * (t - t_0)))

Fields

tau: Duration

The time constant, tau gives the correlation time, or the time over which the intensity of the time correlation will fade to 1/e of its prior value. (This is sometimes incorrectly referred to as the “half-life” of the process.)

process_noise: f64

prev_epoch: Option<Epoch>

Epoch of the previous realization, used to compute the time delta for the process noise.

init_sample: Option<f64>

Sample of previous realization

Implementations

impl GaussMarkov

pub const ZERO: Self = _

Zero noise Gauss-Markov process.

pub fn new(tau: Duration, process_noise: f64) -> Result<Self, ConfigError>

Create a new first order Gauss-Markov process.

Arguments

• tau - The time constant, tau gives the correlation time, or the time over which the intensity of the time correlation will fade to 1/e of its prior value.
• process_noise - process noise of the system.

pub fn default_range_km() -> Self

Default Gauss Markov noise of the Deep Space Network, as per DESCANSO Chapter 3, Table 3-3. Used the range value of 60 cm over a 60 second average.

pub fn default_doppler_km_s() -> Self

Default Gauss Markov noise of the Deep Space Network, as per DESCANSO Chapter 3, Table 3-3. Used the Doppler value of 0.03 mm/s over a 60 second average.

Trait Implementations

impl Clone for GaussMarkov

fn clone(&self) -> GaussMarkov

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ConfigRepr for GaussMarkov

fn load<P>(path: P) -> Result<Self, ConfigError>

where P: AsRef<Path>,

Builds the configuration representation from the path to a yaml

fn load_many<P>(path: P) -> Result<Vec<Self>, ConfigError>

where P: AsRef<Path>,

Builds a sequence of “Selves” from the provided path to a yaml

fn load_named<P>(path: P) -> Result<BTreeMap<String, Self>, ConfigError>

where P: AsRef<Path>,

Builds a map of names to “selves” from the provided path to a yaml

fn loads_many(data: &str) -> Result<Vec<Self>, ConfigError>

Builds a sequence of “Selves” from the provided string of a yaml

fn loads_named(data: &str) -> Result<BTreeMap<String, Self>, ConfigError>

Builds a sequence of “Selves” from the provided string of a yaml

impl Debug for GaussMarkov

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for GaussMarkov

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for GaussMarkov

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

Formats the value using the given formatter.

impl Mul<f64> for GaussMarkov

fn mul(self, rhs: f64) -> Self::Output

Scale the Gauss Markov process by a constant, maintaining the same time constant.

type Output = GaussMarkov

The resulting type after applying the * operator.

impl PartialEq for GaussMarkov

fn eq(&self, other: &GaussMarkov) -> bool

Tests for self and other values to be equal, and is used by ==.

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

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

impl Serialize for GaussMarkov

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Stochastics for GaussMarkov

fn sample<R: Rng>(&mut self, epoch: Epoch, rng: &mut R) -> f64

Return the next bias sample.

fn covariance(&self, _epoch: Epoch) -> f64

Return the variance of this stochastic noise model at a given time.

impl Copy for GaussMarkov

impl StructuralPartialEq for GaussMarkov