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    AlphaBetaGamma

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    pub(all) struct AlphaBetaGamma {
      alpha : Double
      beta : Double
      gamma : Double
      position : Double
      velocity : Double
      acceleration : Double
    } derive(
    Debug    )

    An alpha-beta-gamma tracker: position, velocity and acceleration of one axis from a position measurement.

    The three gains trade noise against lag the way a Kalman filter does with a fixed model: alpha corrects the position, beta the velocity and gamma the acceleration. The preset is the critically damped set for a steady-state model of a constant acceleration.

    AlphaBetaGamma::new

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    fn AlphaBetaGamma::new(alpha? : Double, beta? : Double, gamma? : Double, position? : Double) -> AlphaBetaGamma raise 
    Refused

    A tracker with the given gains and starting position.

    AlphaBetaGamma::to_repr

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    AlphaBetaGamma::update

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    fn AlphaBetaGamma::update(self : AlphaBetaGamma, measurement~ : Double, dt~ : Double) -> 
    Sample

    The state after folding in one position measurement.

    Complementary

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    pub(all) struct Complementary {
      alpha : Double
      angle : Double
    } derive(
    Debug    )

    A complementary filter for one angle: the gyro is trusted for the fast motion, the measurement for the slow drift.

    alpha is the fraction of the integrated rate kept each period. At 0.98 with a period of a hundredth of a second the time constant is about half a second, which is the usual balance for a hobby-grade inertial sensor.

    Complementary::angle

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    fn Complementary::angle(self : Complementary) -> Double

    The current angle.

    Complementary::new

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    fn Complementary::new(alpha? : Double, angle? : Double) -> Complementary raise 
    Refused

    A complementary filter with the given blend and starting angle.

    Complementary::to_repr

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    Complementary::update

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    fn Complementary::update(self : Complementary, rate~ : Double, measurement~ : Double, dt~ : Double) -> Double

    The corrected angle after one period.

    ExtendedKalman

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    An extended Kalman filter: the linear recursion with the Jacobians the model supplies, whether analytically or by the contract's default differences.

    ExtendedKalman::correct

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    ExtendedKalman::covariance

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    ExtendedKalman::new

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    An extended Kalman filter for any plant that implements the model contract.

    ExtendedKalman::predict

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    fn ExtendedKalman::predict(self : ExtendedKalman, u : 
    Vec    , dt : Double) -> Unit

    ExtendedKalman::state

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    ExtendedKalman::to_repr

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    Kalman

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    A linear Kalman filter for a discrete state-space plant.
    impl Estimator for Kalman

    Kalman::correct

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    Kalman::covariance

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    Kalman::new

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    A Kalman filter for model with the given noise covariances and prior.

    Kalman::predict

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    fn Kalman::predict(self : Kalman, u : 
    Vec    , dt : Double) -> Unit

    Kalman::state

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    ParticleFilter

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    A particle filter over any plant that implements the model contract.

    The particles are drawn around the prior at construction and then carried by the model itself, so the filter never assumes the state distribution stays Gaussian. Resampling is systematic and only runs when the effective sample size falls below half the cloud, which keeps the diversity a plain resample-every-step filter loses.

    ParticleFilter::correct

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    ParticleFilter::covariance

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    ParticleFilter::new

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    A particle filter with samples particles drawn around state.

    ParticleFilter::particles

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    The particles, for a caller that plots the cloud.

    ParticleFilter::predict

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    fn ParticleFilter::predict(self : ParticleFilter, u : 
    Vec    , dt : Double) -> Unit

    ParticleFilter::state

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    ParticleFilter::to_repr

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    ParticleFilter::weights

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    fn ParticleFilter::weights(self : ParticleFilter) -> Array[Double]

    The weight of each particle.

    UnscentedConfig

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    pub(all) struct UnscentedConfig {
      alpha : Double
      beta : Double
      kappa : Double
    } derive(
    Debug    )

    The spread of the sigma points in the unscented transform.

    alpha sets how far the points sit from the mean, beta folds in the knowledge that the distribution is Gaussian, and kappa is the usual secondary scaling. The preset is the one the reference implementations use.

    UnscentedConfig::to_repr

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    UnscentedKalman

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    An unscented Kalman filter.

    UnscentedKalman::correct

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    UnscentedKalman::covariance

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    UnscentedKalman::new

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    An unscented Kalman filter for any plant that implements the model contract.

    UnscentedKalman::predict

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    fn UnscentedKalman::predict(self : UnscentedKalman, u : 
    Vec    , dt : Double) -> Unit

    UnscentedKalman::state

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    UnscentedKalman::to_repr

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    rts

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    The Rauch-Tung-Striebel smoother: the forward filter's estimates refined by a backward pass over the same record.

    states and covariances are the filtered estimates, transitions the state matrices between consecutive steps, and process the process covariance. The result is the smoothed state and covariance at every step, whose covariances are never larger than the filtered ones.

    unscented_config

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    let unscented_config : UnscentedConfig

    The preset spread: a narrow alpha, the Gaussian beta and a zero kappa.

    Source Files