milosgajdos / go-estimate

State estimation and filtering algorithms in Go

go-estimate: State estimation and filtering algorithms in Go

This package offers a small suite of basic filtering algorithms written in Go. It currently provides the implementations of the following filters and estimators:

• Bootstrap Filter also known as SIR Particle filter
• Unscented Kalman Filter also known as Sigma-point filter
• Extended Kalman Filter also known as Non-linear Kalman Filter
  • Iterated Extended Kalman Filter
• Kalman Filter also known as Linear Kalman Filter

In addition it provides an implementation of Rauch–Tung–Striebel smoothing for Kalman filter, which is an optimal Gaussian smoothing algorithm. There are variants for both LKF (Linear Kalman Filter) and EKF (Extended Kalman Filter) implemented in the smooth package. UKF smoothing will be implemented in the future.

Get started

Get the package:

$ go get github.com/milosgajdos/go-estimate

Get dependencies:

$ make dep

Run unit tests:

$ make test

You can find various examples of usage in go-estimate-examples.

TODO

• Square Root filter
• Information Filter
• Smoothing
  • Rauch–Tung–Striebel for both KF and EKF has been implemented in smooth package

Contributing

YES PLEASE!

go-estimate's Issues

kalman filtering for non fixed time delta updates

Hi, I was using kalman filter, where I need to call predict but at varying timesteps. I am referring to example where fixed delta t are used for state propagation. My measurement updates and predictions are async and can come at varying time intervals so first update can be called at 1s, next at 5s, and next at 6s... so on. from the source code it seems not doable as m ie. model is private and is fixed during initialization, now I can change A matrix(reflecting delta t multiplication) in model itself but this won't be reflected in kf object, as its pass by value, not pass by pointer. Can you please suggest, if there already exists something for this in library or a workaround.

module `gonum.org/v1/plot.New()` function signature mismatch

myproject/vendor/github.com/milosgajdos/go-estimate/sim/plot.go:35:9: assignment mismatch: 2 variables but plot.New returns 1 values
myproject/vendor/github.com/milosgajdos/go-estimate/sim/plot.go:43:14: assignment mismatch: 2 variables but plot.NewLegend returns 1 values

Link to file https://github.com/milosgajdos/go-estimate/blob/master/sim/plot.go#L35

Running go mod tidy does not fix it. I'm on go version go1.16.3 linux/amd64.

How is this not on awesome-go?

Really, get it up there!

interface definitions with nomenclature

It would be of great use if interfaces where arguments are ambiguous, for example Observer and Filter, named their arguments according to control theory typical nomenclature.

This is current interface definition

// Filter is a dynamical system filter.
type Filter interface {
	// Predict estimates the next internal state of the system
	Predict(mat.Vector, mat.Vector) (Estimate, error)
	// Update updates the system state based on external measurement
	Update(mat.Vector, mat.Vector, mat.Vector) (Estimate, error)
}

// Propagator propagates internal state of the system to the next step
type Propagator interface {
	// Propagate propagates internal state of the system to the next step
	Propagate(mat.Vector, mat.Vector, mat.Vector) (mat.Vector, error)
}

// Observer observes external state (output) of the system
type Observer interface {
	// Observe observes external state of the system
	Observe(mat.Vector, mat.Vector, mat.Vector) (mat.Vector, error)
}

Problems with this definition include:

• All arguments are mat.Vector. What convention is being used?
• Comments do not help either. Overall lackluster documentation

Other interfaces with this problem:

• filter.DiscreteModel. This one is fixed easy: StateMatrix() (A mat.Matrix). A,B,C and D matrices are standard nomenclature for these matrices

Possible solutions

Below is a mix of adding a little bit of documentation + naming the variables.

type Filter interface {
	// Predict estimates the next internal state of the system. x is state, u are inputs
	Predict(x,u mat.Vector) (filter.Estimate, error)
	// Update updates the system state based on external measurement. x is state, u are inputs, z is the measurement
	Update(x, u, z mat.Vector) (filter.Estimate, error) 
}

Cannot run bf.go example

When I copy bf.go into my project and run it I get the following output:
../esp/go/pkg/mod/github.com/milosgajdos83/[email protected]/noise/gaussian.go:44:32: g.dist.CovarianceMatrix(nil) used as value
But if I run it from the examples dir it works as expected.

Question on motion model for drones

Hi,

We are looking for an UKF for motion modelling a drone path where degrees of freedom are very high. Just wanted to check this library is generic enough to incorporate any complex motion model.