backwardn/go-estimate

State estimation and filtering algorithms in Go

go-estimate

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!