kf

package

v0.1.2 Latest Latest Go to latest Published: Feb 20, 2023 License: Apache-2.0 Imports: 5 Imported by: 3

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Repository

github.com/milosgajdos/go-estimate

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README ¶

[Linear] Kalman Filter

This package implements Kalman Filter.

Example output

Documentation ¶

Index ¶

type KF
- func New(m filter.DiscreteModel, init filter.InitCond, z, wn filter.Noise) (*KF, error)

Constants ¶

This section is empty.

Variables ¶

This section is empty.

Functions ¶

This section is empty.

Types ¶

type KF ¶

type KF struct {
	// contains filtered or unexported fields
}

KF is Kalman Filter

func New ¶

func New(m filter.DiscreteModel, init filter.InitCond, z, wn filter.Noise) (*KF, error)

New creates new KF and returns it. It accepts the following parameters:

m: dynamical system model
init: initial condition of the filter
z: disturbance input a.k.a. process noise corresponding to E disturbance matrix
wn: output noise a.k.a. measurement noise
c: KF configuration (contains propagation and observation matrices)

It returns error if either of the following conditions is met:

invalid model is given: model dimensions must be positive integers
invalid state or output noise is given: noise covariance must either be nil or match the model dimensions

func (*KF) Cov ¶

func (k *KF) Cov() mat.Symmetric

Cov returns KF covariance

func (*KF) Gain ¶

func (k *KF) Gain() mat.Matrix

Gain returns Kalman gain

func (*KF) Model ¶

func (k *KF) Model() filter.Model

Model returns KF models

func (*KF) OutputNoise ¶

func (k *KF) OutputNoise() filter.Noise

OutputNoise retruns output noise

func (*KF) Predict ¶

func (k *KF) Predict(x, u mat.Vector) (filter.Estimate, error)

Predict calculates the next system state given the state x and input u and returns its estimate. It first generates new sigma points around x and then attempts to propagate them to the next step. It returns error if it either fails to generate or propagate the sigma points (and x) to the next step.

func (*KF) Run ¶

func (k *KF) Run(x, u, z mat.Vector) (filter.Estimate, error)

Run runs one step of KF for given state x, input u and measurement z. It corrects system state x using measurement z and returns new system estimate. It returns error if it either fails to propagate or correct state x.

func (*KF) SetCov ¶

func (k *KF) SetCov(cov mat.Symmetric) error

SetCov sets KF covariance matrix to cov. It returns error if either cov is nil or its dimensions are not the same as KF covariance dimensions.

func (*KF) StateNoise ¶

func (k *KF) StateNoise() filter.Noise

StateNoise retruns state noise

func (*KF) Update ¶

func (k *KF) Update(x, u, ym mat.Vector) (filter.Estimate, error)

Update corrects state x using the measurement z, given control intput u and returns corrected estimate. It returns error if either invalid state was supplied or if it fails to calculate system output estimate.

Source Files ¶

View all Source files

kf.go

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